Development and Implementation of Dynamic Scripts to Execute Cycled GSI/WRF Forecasts

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley; Srikishen, Jayanthi; Berndt, Emily; Li, Xuanli; Watson, Leela

2014-01-01

The Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model and Gridpoint Statistical Interpolation (GSI) data assimilation (DA) are the operational systems that make up the North American Mesoscale (NAM) model and the NAM Data Assimilation System (NDAS) analysis used by National Weather Service forecasters. The Developmental Testbed Center (DTC) manages and distributes the code for the WRF and GSI, but it is up to individual researchers to link the systems together and write scripts to run the systems, which can take considerable time for those not familiar with the code. The objective of this project is to develop and disseminate a set of dynamic scripts that mimic the unique cycling configuration of the operational NAM to enable researchers to develop new modeling and data assimilation techniques that can be easily transferred to operations. The current version of the SPoRT GSI/WRF Scripts (v3.0.1) is compatible with WRF v3.3 and GSI v3.0.

Does the uncertainty in the representation of terrestrial water flows affect precipitation predictability? A WRF-Hydro ensemble analysis for Central Europe

NASA Astrophysics Data System (ADS)

Arnault, Joel; Rummler, Thomas; Baur, Florian; Lerch, Sebastian; Wagner, Sven; Fersch, Benjamin; Zhang, Zhenyu; Kerandi, Noah; Keil, Christian; Kunstmann, Harald

2017-04-01

Precipitation predictability can be assessed by the spread within an ensemble of atmospheric simulations being perturbed in the initial, lateral boundary conditions and/or modeled processes within a range of uncertainty. Surface-related processes are more likely to change precipitation when synoptic forcing is weak. This study investigates the effect of uncertainty in the representation of terrestrial water flows on precipitation predictability. The tools used for this investigation are the Weather Research and Forecasting (WRF) model and its hydrologically-enhanced version WRF-Hydro, applied over Central Europe during April-October 2008. The WRF grid is that of COSMO-DE, with a resolution of 2.8 km. In WRF-Hydro, the WRF grid is coupled with a sub-grid at 280 m resolution to resolve lateral terrestrial water flows. Vertical flow uncertainty is considered by modifying the parameter controlling the partitioning between surface runoff and infiltration in WRF, and horizontal flow uncertainty is considered by comparing WRF with WRF-Hydro. Precipitation predictability is deduced from the spread of an ensemble based on three turbulence parameterizations. Model results are validated with E-OBS precipitation and surface temperature, ESA-CCI soil moisture, FLUXNET-MTE surface evaporation and GRDC discharge. It is found that the uncertainty in the representation of terrestrial water flows is more likely to significantly affect precipitation predictability when surface flux spatial variability is high. In comparison to the WRF ensemble, WRF-Hydro slightly improves the adjusted continuous ranked probability score of daily precipitation. The reproduction of observed daily discharge with Nash-Sutcliffe model efficiency coefficients up to 0.91 demonstrates the potential of WRF-Hydro for flood forecasting.

High-resolution dynamically downscaled projections of precipitation in the mid and late 21st century over North America: DYNAMICAL DOWNSCALING AT 12 KM

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Jiali; Kotamarthi, Veerabhadra R.

This study performs high spatial resolution (12 km) Weather Research and Forecasting (WRF) simulations over a very large domain (7200 × 6180 km2, covering much of North America) to explore changes in mean and extreme precipitation in the mid and late 21st century under Representative Concentration Pathways 4.5 (RCP 4.5) and 8.5 (RCP 8.5). We evaluate WRF model performance for a historical simulation and future projections when applying the Community Climate System Model version 4 (CCSM4) as initial and boundary conditions with and without a bias correction. WRF simulations using boundary and initial conditions from both versions of CCSM4, showmore » smaller biases versus evaluation data sets than does CCSM4 over western North America. WRF simulations also improve spatial details of precipitation over much of North America. However, driving the WRF with the bias corrected CCSM4 does not always reduce the bias. WRF-projected changes in precipitation include decreasing intensity over the U.S. Southwest, increasing intensity over the eastern United Sates and most of Canada, and an increase in the number of days with heavy precipitation over much of NA. Projected precipitation changes are more evident in the late 21st century than the mid 21st century, and they are more evident under RCP 8.5 than RCP 4.5 in the late 21st century. Uncertainties in the projected changes in precipitation due to different warming scenarios are non-negligible. Differences in summer precipitation changes between WRF and CCSM4 are significant over most of the United States.« less

High-resolution dynamically downscaled projections of precipitation in the mid and late 21st century over North America

DOE Office of Scientific and Technical Information (OSTI.GOV)

None, None

This study performs high-spatial-resolution (12 km) Weather Research and Forecasting (WRF) simulations over a very large domain (7200 km × 6180 km, covering much of North America) to explore changes in mean and extreme precipitation in the mid and late 21st century under Representative Concentration Pathways 4.5 (RCP 4.5) and 8.5 (RCP 8.5). We evaluate WRF model performance for a historical simulation and future projections, applying the Community Climate System Model version 4 (CCSM4) as initial and boundary conditions with and without a bias correction. WRF simulations using boundary and initial conditions from both versions of CCSM4 show smaller biasesmore » versus evaluation data sets than does CCSM4 over western North America. WRF simulations also improve spatial details of precipitation over much of North America. However, driving the WRF with the bias-corrected CCSM4 does not always reduce the bias. WRF-projected changes in precipitation include decreasing intensity over the southwestern United States, increasing intensity over the eastern United States and most of Canada, and an increase in the number of days with heavy precipitation over much of North America. Projected precipitation changes are more evident in the late 21st century than the mid 21st century, and they are more evident under RCP 8.5 than under RCP 4.5 in the late 21st century. Uncertainties in the projected changes in precipitation due to different warming scenarios are non-negligible. Differences in summer precipitation changes between WRF and CCSM4 are significant over most of the United States.« less

High-resolution dynamically downscaled projections of precipitation in the mid and late 21st century over North America

DOE PAGES

None, None

2015-07-29

This study performs high-spatial-resolution (12 km) Weather Research and Forecasting (WRF) simulations over a very large domain (7200 km × 6180 km, covering much of North America) to explore changes in mean and extreme precipitation in the mid and late 21st century under Representative Concentration Pathways 4.5 (RCP 4.5) and 8.5 (RCP 8.5). We evaluate WRF model performance for a historical simulation and future projections, applying the Community Climate System Model version 4 (CCSM4) as initial and boundary conditions with and without a bias correction. WRF simulations using boundary and initial conditions from both versions of CCSM4 show smaller biasesmore » versus evaluation data sets than does CCSM4 over western North America. WRF simulations also improve spatial details of precipitation over much of North America. However, driving the WRF with the bias-corrected CCSM4 does not always reduce the bias. WRF-projected changes in precipitation include decreasing intensity over the southwestern United States, increasing intensity over the eastern United States and most of Canada, and an increase in the number of days with heavy precipitation over much of North America. Projected precipitation changes are more evident in the late 21st century than the mid 21st century, and they are more evident under RCP 8.5 than under RCP 4.5 in the late 21st century. Uncertainties in the projected changes in precipitation due to different warming scenarios are non-negligible. Differences in summer precipitation changes between WRF and CCSM4 are significant over most of the United States.« less

The SPoRT-WRF: Evaluating the Impact of NASA Datasets on Convective Forecasts

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley; Kozlowski, Danielle; Case, Jonathan; Molthan, Andrew

2012-01-01

Short-term Prediction Research and Transition (SPoRT) seeks to improve short-term, regional weather forecasts using unique NASA products and capabilities SPoRT has developed a unique, real-time configuration of the NASA Unified Weather Research and Forecasting (WRF)WRF (ARW) that integrates all SPoRT modeling research data: (1) 2-km SPoRT Sea Surface Temperature (SST) Composite, (2) 3-km LIS with 1-km Greenness Vegetation Fraction (GVFs) (3) 45-km AIRS retrieved profiles. Transitioned this real-time forecast to NOAA's Hazardous Weather Testbed (HWT) as deterministic model at Experimental Forecast Program (EFP). Feedback from forecasters/participants and internal evaluation of SPoRT-WRF shows a cool, dry bias that appears to suppress convection likely related to methodology for assimilation of AIRS profiles Version 2 of the SPoRT-WRF will premier at the 2012 EFP and include NASA physics, cycling data assimilation methodology, better coverage of precipitation forcing, and new GVFs

Integrated Modeling of Aerosol, Cloud, Precipitation and Land Processes at Satellite-Resolved Scales

NASA Technical Reports Server (NTRS)

Peters-Lidard, Christa; Tao, Wei-Kuo; Chin, Mian; Braun, Scott; Case, Jonathan; Hou, Arthur; Kumar, Anil; Kumar, Sujay; Lau, William; Matsui, Toshihisa;

Development of the WRF-CO2 4D-Var assimilation system v1.0

NASA Astrophysics Data System (ADS)

Zheng, Tao; French, Nancy H. F.; Baxter, Martin

2018-05-01

Regional atmospheric CO2 inversions commonly use Lagrangian particle trajectory model simulations to calculate the required influence function, which quantifies the sensitivity of a receptor to flux sources. In this paper, an adjoint-based four-dimensional variational (4D-Var) assimilation system, WRF-CO2 4D-Var, is developed to provide an alternative approach. This system is developed based on the Weather Research and Forecasting (WRF) modeling system, including the system coupled to chemistry (WRF-Chem), with tangent linear and adjoint codes (WRFPLUS), and with data assimilation (WRFDA), all in version 3.6. In WRF-CO2 4D-Var, CO2 is modeled as a tracer and its feedback to meteorology is ignored. This configuration allows most WRF physical parameterizations to be used in the assimilation system without incurring a large amount of code development. WRF-CO2 4D-Var solves for the optimized CO2 flux scaling factors in a Bayesian framework. Two variational optimization schemes are implemented for the system: the first uses the limited memory Broyden-Fletcher-Goldfarb-Shanno (BFGS) minimization algorithm (L-BFGS-B) and the second uses the Lanczos conjugate gradient (CG) in an incremental approach. WRFPLUS forward, tangent linear, and adjoint models are modified to include the physical and dynamical processes involved in the atmospheric transport of CO2. The system is tested by simulations over a domain covering the continental United States at 48 km × 48 km grid spacing. The accuracy of the tangent linear and adjoint models is assessed by comparing against finite difference sensitivity. The system's effectiveness for CO2 inverse modeling is tested using pseudo-observation data. The results of the sensitivity and inverse modeling tests demonstrate the potential usefulness of WRF-CO2 4D-Var for regional CO2 inversions.

WRF-Cordex simulations for Europe: mean and extreme precipitation for present and future climates

NASA Astrophysics Data System (ADS)

Cardoso, Rita M.; Soares, Pedro M. M.; Miranda, Pedro M. A.

2013-04-01

The Weather Research and Forecast (WRF-ARW) model, version 3.3.1, was used to perform the European domain Cordex simulations, at 50km resolution. A first simulation, forced by ERA-Interim (1989-2009), was carried out to evaluate the models performance to represent the mean and extreme precipitation in present European climate. This evaluation is based in the comparison of WRF results against the ECAD regular gridded dataset of daily precipitation. Results are comparable to recent studies with other models for the European region, at this resolution. For the same domain a control and a future scenario (RCP8.5) simulation was performed to assess the climate change impact on the mean and extreme precipitation. These regional simulations were forced by EC-EARTH model results, and, encompass the periods from 1960-2006 and 2006-2100, respectively.

Update of global TC simulations using a variable resolution non-hydrostatic model

NASA Astrophysics Data System (ADS)

Park, S. H.

2017-12-01

Using in a variable resolution meshes in MPAS during 2017 summer., Tropical cyclone (TC) forecasts are simulated. Two physics suite are tested to explore performance and bias of each physics suite for TC forecasting. A WRF physics suite is selected from experience on weather forecasting and CAM (Community Atmosphere Model) physics is taken from a AMIP type climate simulation. Based on the last year results from CAM5 physical parameterization package and comparing with WRF physics, we investigated a issue with intensity bias using updated version of CAM physics (CAM6). We also compared these results with coupled version of TC simulations. During this talk, TC structure will be compared specially around of boundary layer and investigate their relationship between TC intensity and different physics package.

A Coupled Surface Nudging Scheme for use in Retrospective ...

EPA Pesticide Factsheets

A surface analysis nudging scheme coupling atmospheric and land surface thermodynamic parameters has been implemented into WRF v3.8 (latest version) for use with retrospective weather and climate simulations, as well as for applications in air quality, hydrology, and ecosystem modeling. This scheme is known as the flux-adjusting surface data assimilation system (FASDAS) developed by Alapaty et al. (2008). This scheme provides continuous adjustments for soil moisture and temperature (via indirect nudging) and for surface air temperature and water vapor mixing ratio (via direct nudging). The simultaneous application of indirect and direct nudging maintains greater consistency between the soil temperature–moisture and the atmospheric surface layer mass-field variables. The new method, FASDAS, consistently improved the accuracy of the model simulations at weather prediction scales for different horizontal grid resolutions, as well as for high resolution regional climate predictions. This new capability has been released in WRF Version 3.8 as option grid_sfdda = 2. This new capability increased the accuracy of atmospheric inputs for use air quality, hydrology, and ecosystem modeling research to improve the accuracy of respective end-point research outcome. IMPACT: A new method, FASDAS, was implemented into the WRF model to consistently improve the accuracy of the model simulations at weather prediction scales for different horizontal grid resolutions, as wel

Overview and Evaluation of the Community Multiscale Air ...

EPA Pesticide Factsheets

The Community Multiscale Air Quality (CMAQ) model is a state-of-the-science air quality model that simulates the emission, transport and fate of numerous air pollutants, including ozone and particulate matter. The Computational Exposure Division (CED) of the U.S. Environmental Protection Agency develops the CMAQ model and periodically releases new versions of the model that include bug fixes and various other improvements to the modeling system. In late 2016 or early 2017, CMAQ version 5.2 will be released. This new version of CMAQ will contain important updates from the current CMAQv5.1 modeling system, along with several instrumented versions of the model (e.g. decoupled direct method and sulfur tracking). Some specific model updates include the implementation of a new wind-blown dust treatment in CMAQv5.2, a significant improvement over the treatment in v5.1 which can severely overestimate wind-blown dust under certain conditions. Several other major updates to the modeling system include an update to the calculation of aerosols; implementation of full halogen chemistry (CMAQv5.1 contains a partial implementation of halogen chemistry); the new carbon bond 6 (CB6) chemical mechanism; updates to cloud model in CMAQ; and a new lightning assimilation scheme for the WRF model which significant improves the placement and timing of convective precipitation in the WRF precipitation fields. Numerous other updates to the modeling system will also be available in v5.2.

Model representations of aerosol layers transported from North America over the Atlantic Ocean during the Two-Column Aerosol Project

NASA Astrophysics Data System (ADS)

Fast, Jerome D.; Berg, Larry K.; Zhang, Kai; Easter, Richard C.; Ferrare, Richard A.; Hair, Johnathan W.; Hostetler, Chris A.; Liu, Ying; Ortega, Ivan; Sedlacek, Arthur; Shilling, John E.; Shrivastava, Manish; Springston, Stephen R.; Tomlinson, Jason M.; Volkamer, Rainer; Wilson, Jacqueline; Zaveri, Rahul A.; Zelenyuk, Alla

2016-08-01

The ability of the Weather Research and Forecasting model with chemistry (WRF-Chem) version 3.7 and the Community Atmosphere Model version 5.3 (CAM5) in simulating profiles of aerosol properties is quantified using extensive in situ and remote sensing measurements from the Two-Column Aerosol Project (TCAP) conducted during July of 2012. TCAP was supported by the U.S. Department of Energy's Atmospheric Radiation Measurement program and was designed to obtain observations within two atmospheric columns; one fixed over Cape Cod, Massachusetts, and the other several hundred kilometers over the ocean. The performance is quantified using most of the available aircraft and surface measurements during July, and 2 days are examined in more detail to identify the processes responsible for the observed aerosol layers. The higher-resolution WRF-Chem model produced more aerosol mass in the free troposphere than the coarser-resolution CAM5 model so that the fraction of aerosol optical thickness above the residual layer from WRF-Chem was more consistent with lidar measurements. We found that the free troposphere layers are likely due to mean vertical motions associated with synoptic-scale convergence that lifts aerosols from the boundary layer. The vertical displacement and the time period associated with upward transport in the troposphere depend on the strength of the synoptic system and whether relatively high boundary layer aerosol concentrations are present where convergence occurs. While a parameterization of subgrid scale convective clouds applied in WRF-Chem modulated the concentrations of aerosols aloft, it did not significantly change the overall altitude and depth of the layers.

Assimilation of Atmospheric InfraRed Sounder (AIRS) Profiles using WRF-Var

NASA Technical Reports Server (NTRS)

Zavodsky, Brad; Jedlovec, Gary J.; Lapenta, William

2008-01-01

The Weather Research and Forecasting (WRF) model contains a three-dimensional variational (3DVAR) assimilation system (WRF-Var), which allows a user to join data from multiple sources into one coherent analysis. WRF-Var combines observations with a background field traditionally generated using a previous model forecast through minimization of a cost function. In data sparse regions, remotely-sensed observations may be able to improve analyses and produce improved forecasts. One such source comes from the Atmospheric Infrared Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), represents one of the most advanced space-based atmospheric sounding systems. The combined AIRS/AMSU system provides radiance measurements used as input to a sophisticated retrieval scheme which has been shown to produce temperature profiles with an accuracy of 1 K over 1 km layers and humidity profiles with accuracy of 15% in 2 km layers in both clear and partly cloudy conditions. The retrieval algorithm also provides estimates of the accuracy of the retrieved values at each pressure level, allowing the user to select profiles based on the required error tolerances of the application. The purpose of this paper is to describe a procedure to optimally assimilate high-resolution AIRS profile data into a regional configuration of the Advanced Research WRF (ARW) version 2.2 using WRF-Var. The paper focuses on development of background error covariances for the regional domain and background field type using gen_be and an optimal methodology for ingesting AIRS temperature and moisture profiles as separate overland and overwater retrievals with different error characteristics in the WRF-Var. The AIRS thermodynamic profiles are obtained from the version 5.0 Earth Observing System (EOS) science team retrieval algorithm and contain information about the quality of each temperature layer. The quality indicators are used to select the highest quality temperature and moisture data for each profile location and pressure level. Analyses are run to produce quasi-real-time regional weather forecasts over the continental U.S. The preliminary assessment of the impact of the AIRS profiles will focus on intelligent use of the quality indicators, optimized tuning of the WRF-Var, and comparison of analysis soundings to radiosondes.

Investigating Marine Boundary Layer Parameterizations by Combining Observations with Models via State Estimation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Delle Monahce, Luca; Clifton, Andrew; Hacker, Joshua

In this project we have improved numerical weather prediction analyses and forecasts of low level winds in the marine boundary layer. This has been accomplished with the following tools; The National Center for Atmospheric Research (NCAR) Weather and Research Forecasting model, WRF, both in his single column (SCM) and three-dimensional (3D) versions; The National Oceanic and Atmospheric Administration (NOAA) Wave Watch III (WWIII); SE algorithms from the Data Assimilation Research Testbed (DART, Anderson et al. 2009); and Observations of key quantities of the lower MBL, including temperature and winds at multiple levels above the sea surface. The experiments with themore » WRF SCM / DART system have lead to large improvements with respect to a standard WRF configuration, which is currently commonly used by the wind energy industry. The single column model appears to be a tool particularly suitable for off-shore wind energy applications given its accuracy, the ability to quantify uncertainty, and the minimal computational resource requirements. In situations where the impact of an upwind wind park may be of interest in a downwind location, a 3D approach may be more suitable. We have demonstrated that with the WRF 3D / DART system the accuracy of wind predictions (and other meteorological parameters) can be improved over a 3D computational domain, and not only at specific locations. All the scripting systems developed in this project (i.e., to run WRF SCM / DART, WRF 3D / DART, and the coupling between WRF and WWIII) and the several modifications and upgrades made to the WRF SCM model will be shared with the broader community.« less

Capabilities of current wildfire models when simulating topographical flow

NASA Astrophysics Data System (ADS)

Kochanski, A.; Jenkins, M.; Krueger, S. K.; McDermott, R.; Mell, W.

2009-12-01

Accurate predictions of the growth, spread and suppression of wild fires rely heavily on the correct prediction of the local wind conditions and the interactions between the fire and the local ambient airflow. Resolving local flows, often strongly affected by topographical features like hills, canyons and ridges, is a prerequisite for accurate simulation and prediction of fire behaviors. In this study, we present the results of high-resolution numerical simulations of the flow over a smooth hill, performed using (1) the NIST WFDS (WUI or Wildland-Urban-Interface version of the FDS or Fire Dynamic Simulator), and (2) the LES version of the NCAR Weather Research and Forecasting (WRF-LES) model. The WFDS model is in the initial stages of development for application to wind flow and fire spread over complex terrain. The focus of the talk is to assess how well simple topographical flow is represented by WRF-LES and the current version of WFDS. If sufficient progress has been made prior to the meeting then the importance of the discrepancies between the predicted and measured winds, in terms of simulated fire behavior, will be examined.

Predicting Power Outages Using Multi-Model Ensemble Forecasts

NASA Astrophysics Data System (ADS)

Cerrai, D.; Anagnostou, E. N.; Yang, J.; Astitha, M.

2017-12-01

Power outages affect every year millions of people in the United States, affecting the economy and conditioning the everyday life. An Outage Prediction Model (OPM) has been developed at the University of Connecticut for helping utilities to quickly restore outages and to limit their adverse consequences on the population. The OPM, operational since 2015, combines several non-parametric machine learning (ML) models that use historical weather storm simulations and high-resolution weather forecasts, satellite remote sensing data, and infrastructure and land cover data to predict the number and spatial distribution of power outages. A new methodology, developed for improving the outage model performances by combining weather- and soil-related variables using three different weather models (WRF 3.7, WRF 3.8 and RAMS/ICLAMS), will be presented in this study. First, we will present a performance evaluation of each model variable, by comparing historical weather analyses with station data or reanalysis over the entire storm data set. Hence, each variable of the new outage model version is extracted from the best performing weather model for that variable, and sensitivity tests are performed for investigating the most efficient variable combination for outage prediction purposes. Despite that the final variables combination is extracted from different weather models, this ensemble based on multi-weather forcing and multi-statistical model power outage prediction outperforms the currently operational OPM version that is based on a single weather forcing variable (WRF 3.7), because each model component is the closest to the actual atmospheric state.

Model representations of aerosol layers transported from North America over the Atlantic Ocean during the Two-Column Aerosol Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fast, Jerome D.; Berg, Larry K.; Zhang, Kai

2016-08-22

The ability of the Weather Research and Forecasting model with chemistry (WRF-Chem) version 3.7 and the Community Atmosphere Model version 5.3 (CAM5) in simulating profiles of aerosol properties is quantified using extensive in situ and remote sensing measurements from the Two Column Aerosol Project (TCAP) conducted during July of 2012. TCAP was supported by the U.S. Department of Energy’s Atmospheric Radiation Measurement program and was designed to obtain observations within two atmospheric columns; one fixed over Cape Cod, Massachusetts and the other several hundred kilometers over the ocean. The performance is quantified using most of the available aircraft and surfacemore » measurements during July, and two days are examined in more detail to identify the processes responsible for the observed aerosol layers. The higher resolution WRF-Chem model produced more aerosol mass in the free troposphere than the coarser resolution CAM5 model so that the fraction of aerosol optical thickness above the residual layer from WRF-Chem was more consistent with lidar measurements. We found that the free troposphere layers are likely due to mean vertical motions associated with synoptic-scale convergence that lifts aerosols from the boundary layer. The vertical displacement and the time period associated with upward transport in the troposphere depend on the strength of the synoptic system and whether relatively high boundary layer aerosol concentrations are present where convergence occurs. While a parameterization of subgrid scale convective clouds applied in WRF-Chem modulated the concentrations of aerosols aloft, it did not significantly change the overall altitude and depth of the layers.« less

Model representations of aerosol layers transported from North America over the Atlantic Ocean during the Two-Column Aerosol Project

DOE PAGES

Fast, Jerome D.; Berg, Larry K.; Zhang, Kai; ...

2016-08-22

The ability of the Weather Research and Forecasting model with chemistry (WRF-Chem) version 3.7 and the Community Atmosphere Model version 5.3 (CAM5) in simulating profiles of aerosol properties is quantified using extensive in situ and remote sensing measurements from the Two-Column Aerosol Project (TCAP) conducted during July of 2012. TCAP was supported by the U.S. Department of Energy's Atmospheric Radiation Measurement program and was designed to obtain observations within two atmospheric columns; one fixed over Cape Cod, Massachusetts, and the other several hundred kilometers over the ocean. The performance is quantified using most of the available aircraft and surface measurementsmore » during July, and 2 days are examined in more detail to identify the processes responsible for the observed aerosol layers. The higher-resolution WRF-Chem model produced more aerosol mass in the free troposphere than the coarser-resolution CAM5 model so that the fraction of aerosol optical thickness above the residual layer from WRF-Chem was more consistent with lidar measurements. We found that the free troposphere layers are likely due to mean vertical motions associated with synoptic-scale convergence that lifts aerosols from the boundary layer. The vertical displacement and the time period associated with upward transport in the troposphere depend on the strength of the synoptic system and whether relatively high boundary layer aerosol concentrations are present where convergence occurs. In conclusion, while a parameterization of subgrid scale convective clouds applied in WRF-Chem modulated the concentrations of aerosols aloft, it did not significantly change the overall altitude and depth of the layers.« less

Modeling the Influence of Hemispheric Transport on Trends in O3 Distributions

EPA Science Inventory

We describe the development and application of the hemispheric version of the CMAQ to examine the influence of long-range pollutant transport on trends in surface level O3 distributions. The WRF-CMAQ model is expanded to hemispheric scales and multi-decadal model simulations were...

Assessing the CAM5 Physics Suite in the WRF-Chem Model: Implementation, Resolution Sensitivity, and a First Evaluation for a Regional Case Study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ma, Po-Lun; Rasch, Philip J.; Fast, Jerome D.

A suite of physical parameterizations (deep and shallow convection, turbulent boundary layer, aerosols, cloud microphysics, and cloud fraction) from the global climate model Community Atmosphere Model version 5.1 (CAM5) has been implemented in the regional model Weather Research and Forecasting with chemistry (WRF-Chem). A downscaling modeling framework with consistent physics has also been established in which both global and regional simulations use the same emissions and surface fluxes. The WRF-Chem model with the CAM5 physics suite is run at multiple horizontal resolutions over a domain encompassing the northern Pacific Ocean, northeast Asia, and northwest North America for April 2008 whenmore » the ARCTAS, ARCPAC, and ISDAC field campaigns took place. These simulations are evaluated against field campaign measurements, satellite retrievals, and ground-based observations, and are compared with simulations that use a set of common WRF-Chem Parameterizations. This manuscript describes the implementation of the CAM5 physics suite in WRF-Chem provides an overview of the modeling framework and an initial evaluation of the simulated meteorology, clouds, and aerosols, and quantifies the resolution dependence of the cloud and aerosol parameterizations. We demonstrate that some of the CAM5 biases, such as high estimates of cloud susceptibility to aerosols and the underestimation of aerosol concentrations in the Arctic, can be reduced simply by increasing horizontal resolution. We also show that the CAM5 physics suite performs similarly to a set of parameterizations commonly used in WRF-Chem, but produces higher ice and liquid water condensate amounts and near-surface black carbon concentration. Further evaluations that use other mesoscale model parameterizations and perform other case studies are needed to infer whether one parameterization consistently produces results more consistent with observations.« less

A parallel calibration utility for WRF-Hydro on high performance computers

NASA Astrophysics Data System (ADS)

Wang, J.; Wang, C.; Kotamarthi, V. R.

2017-12-01

A successful modeling of complex hydrological processes comprises establishing an integrated hydrological model which simulates the hydrological processes in each water regime, calibrates and validates the model performance based on observation data, and estimates the uncertainties from different sources especially those associated with parameters. Such a model system requires large computing resources and often have to be run on High Performance Computers (HPC). The recently developed WRF-Hydro modeling system provides a significant advancement in the capability to simulate regional water cycles more completely. The WRF-Hydro model has a large range of parameters such as those in the input table files — GENPARM.TBL, SOILPARM.TBL and CHANPARM.TBL — and several distributed scaling factors such as OVROUGHRTFAC. These parameters affect the behavior and outputs of the model and thus may need to be calibrated against the observations in order to obtain a good modeling performance. Having a parameter calibration tool specifically for automate calibration and uncertainty estimates of WRF-Hydro model can provide significant convenience for the modeling community. In this study, we developed a customized tool using the parallel version of the model-independent parameter estimation and uncertainty analysis tool, PEST, to enabled it to run on HPC with PBS and SLURM workload manager and job scheduler. We also developed a series of PEST input file templates that are specifically for WRF-Hydro model calibration and uncertainty analysis. Here we will present a flood case study occurred in April 2013 over Midwest. The sensitivity and uncertainties are analyzed using the customized PEST tool we developed.

Evaluation of weather research and forecasting model parameterizations under sea-breeze conditions in a North Sea coastal environment

NASA Astrophysics Data System (ADS)

Salvador, Nadir; Reis, Neyval Costa; Santos, Jane Meri; Albuquerque, Taciana Toledo de Almeida; Loriato, Ayres Geraldo; Delbarre, Hervé; Augustin, Patrick; Sokolov, Anton; Moreira, Davidson Martins

2016-12-01

Three atmospheric boundary layer (ABL) schemes and two land surface models that are used in the Weather Research and Forecasting (WRF) model, version 3.4.1, were evaluated with numerical simulations by using data from the north coast of France (Dunkerque). The ABL schemes YSU (Yonsei University), ACM2 (Asymmetric Convective Model version 2), and MYJ (Mellor-Yamada-Janjic) were combined with two land surface models, Noah and RUC (Rapid Update Cycle), in order to determine the performances under sea-breeze conditions. Particular attention is given in the determination of the thermal internal boundary layer (TIBL), which is very important in air pollution scenarios. The other physics parameterizations used in the model were consistent for all simulations. The predictions of the sea-breeze dynamics output from the WRF model were compared with observations taken from sonic detection and ranging, light detection and ranging systems and a meteorological surface station to verify that the model had reasonable accuracy in predicting the behavior of local circulations. The temporal comparisons of the vertical and horizontal wind speeds and wind directions predicted by the WRF model showed that all runs detected the passage of the sea-breeze front. However, except for the combination of MYJ and Noah, all runs had a time delay compared with the frontal passage measured by the instruments. The proposed study shows that the synoptic wind attenuated the intensity and penetration of the sea breeze. This provided changes in the vertical mixing in a short period of time and on soil temperature that could not be detected by the WRF model simulations with the computational grid used. Additionally, among the tested schemes, the combination of the localclosure MYJ scheme with the land surface Noah scheme was able to produce the most accurate ABL height compared with observations, and it was also able to capture the TIBL.

A new WRF-Chem treatment for studying regional-scale impacts of cloud processes on aerosol and trace gases in parameterized cumuli

DOE Office of Scientific and Technical Information (OSTI.GOV)

Berg, L. K.; Shrivastava, M.; Easter, R. C.

A new treatment of cloud effects on aerosol and trace gases within parameterized shallow and deep convection, and aerosol effects on cloud droplet number, has been implemented in the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) version 3.2.1 that can be used to better understand the aerosol life cycle over regional to synoptic scales. The modifications to the model include treatment of the cloud droplet number mixing ratio; key cloud microphysical and macrophysical parameters (including the updraft fractional area, updraft and downdraft mass fluxes, and entrainment) averaged over the population of shallow clouds, or a single deep convectivemore » cloud; and vertical transport, activation/resuspension, aqueous chemistry, and wet removal of aerosol and trace gases in warm clouds. These changes have been implemented in both the WRF-Chem chemistry packages as well as the Kain–Fritsch (KF) cumulus parameterization that has been modified to better represent shallow convective clouds. Testing of the modified WRF-Chem has been completed using observations from the Cumulus Humilis Aerosol Processing Study (CHAPS). The simulation results are used to investigate the impact of cloud–aerosol interactions on regional-scale transport of black carbon (BC), organic aerosol (OA), and sulfate aerosol. Based on the simulations presented here, changes in the column-integrated BC can be as large as –50% when cloud–aerosol interactions are considered (due largely to wet removal), or as large as +40% for sulfate under non-precipitating conditions due to sulfate production in the parameterized clouds. The modifications to WRF-Chem are found to account for changes in the cloud droplet number concentration (CDNC) and changes in the chemical composition of cloud droplet residuals in a way that is consistent with observations collected during CHAPS. Efforts are currently underway to port the changes described here to the latest version of WRF-Chem, and it is anticipated that they will be included in a future public release of WRF-Chem.« less

A new WRF-Chem treatment for studying regional-scale impacts of cloud processes on aerosol and trace gases in parameterized cumuli

DOE PAGES

Berg, L. K.; Shrivastava, M.; Easter, R. C.; ...

2015-02-24

A new treatment of cloud effects on aerosol and trace gases within parameterized shallow and deep convection, and aerosol effects on cloud droplet number, has been implemented in the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) version 3.2.1 that can be used to better understand the aerosol life cycle over regional to synoptic scales. The modifications to the model include treatment of the cloud droplet number mixing ratio; key cloud microphysical and macrophysical parameters (including the updraft fractional area, updraft and downdraft mass fluxes, and entrainment) averaged over the population of shallow clouds, or a single deep convectivemore » cloud; and vertical transport, activation/resuspension, aqueous chemistry, and wet removal of aerosol and trace gases in warm clouds. These changes have been implemented in both the WRF-Chem chemistry packages as well as the Kain–Fritsch (KF) cumulus parameterization that has been modified to better represent shallow convective clouds. Testing of the modified WRF-Chem has been completed using observations from the Cumulus Humilis Aerosol Processing Study (CHAPS). The simulation results are used to investigate the impact of cloud–aerosol interactions on regional-scale transport of black carbon (BC), organic aerosol (OA), and sulfate aerosol. Based on the simulations presented here, changes in the column-integrated BC can be as large as –50% when cloud–aerosol interactions are considered (due largely to wet removal), or as large as +40% for sulfate under non-precipitating conditions due to sulfate production in the parameterized clouds. The modifications to WRF-Chem are found to account for changes in the cloud droplet number concentration (CDNC) and changes in the chemical composition of cloud droplet residuals in a way that is consistent with observations collected during CHAPS. Efforts are currently underway to port the changes described here to the latest version of WRF-Chem, and it is anticipated that they will be included in a future public release of WRF-Chem.« less

The Transition of High-Resolution NASA MODIS Sea Surface Temperatures into the WRF Environmental Modeling System

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; Jedlove, Gary J.; Santos, Pablo; Medlin, Jeffrey M.; Rozumalski, Robert A.

2009-01-01

The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed a Moderate Resolution Imaging Spectroradiometer (MODIS) sea surface temperature (SST) composite at 2-km resolution that has been implemented in version 3 of the National Weather Service (NWS) Weather Research and Forecasting (WRF) Environmental Modeling System (EMS). The WRF EMS is a complete, full physics numerical weather prediction package that incorporates dynamical cores from both the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM). The installation, configuration, and execution of either the ARW or NMM models is greatly simplified by the WRF EMS to encourage its use by NWS Weather Forecast Offices (WFOs) and the university community. The WRF EMS is easy to run on most Linux workstations and clusters without the need for compilers. Version 3 of the WRF EMS contains the most recent public release of the WRF-NMM and ARW modeling system (version 3 of the ARW is described in Skamarock et al. 2008), the WRF Pre-processing System (WPS) utilities, and the WRF Post-Processing program. The system is developed and maintained by the NWS National Science Operations Officer Science and Training Resource Coordinator. To initialize the WRF EMS with high-resolution MODIS SSTs, SPoRT developed the composite product consisting of MODIS SSTs over oceans and large lakes with the NCEP Real-Time Global (RTG) filling data over land points. Filling the land points is required due to minor inconsistencies between the WRF land-sea mask and that used to generate the MODIS SST composites. This methodology ensures a continuous field that adequately initializes all appropriate arrays in WRF. MODIS composites covering the Gulf of Mexico, western Atlantic Ocean and the Caribbean are generated daily at 0400, 0700, 1600, and 1900 UTC corresponding to overpass times of the NASA Aqua and Terra polar orbiting satellites. The MODIS SST product is output in gridded binary-1 (GRIB-1) data format for a seamless incorporation into WRF via the WPS utilities. The full-resolution, 1-km MODIS product is sub-sampled to 2-km grid spacing due to limitations in handling very large dimensions in the GRIB-1 data format. The GRIB-1 files are posted online at ftp://ftp.nsstc.org/sstcomp/WRF/, which is directly accessed by the WRF EMS scripts. The MODIS SST composites are also downloaded to the EMS data server, which is accessible by the WRF EMS users and NWS WFOs. The SPoRT MODIS SST composite provides the model with superior detail of the ocean gradients around Florida and surrounding waters, whereas the operational RTG SST typically depicts a relatively smooth field and is not able to capture sharp horizontal gradients in SST. Differences of 2-3 C are common over small horizontal distances, leading to enhanced SST gradients on either side of the Gulf Stream and along the edges of the cooler shelf waters. These sharper gradients can in turn produce atmospheric responses in simulated temperature and wind fields as depicted in LaCasse et al. Differences in atmospheric verification statistics over a several month study were generally small in the vicinity of south Florida; however, the validation of SSTs at specific buoy locations revealed important improvements in the biases and RMS errors, especially in the vicinity of the cooler shelf waters off the east-central Florida coast. A current weakness in the MODIS SST product is the occurrence of occasional discontinuities caused by high latency in SST coverage due to persistent cloud cover. An enhanced method developed by Jedlovec et al. (2009, GHRSST User Symposium) reduces the occurrence of these problems by adding Advanced Microwave Scanning Radiometer -- EOS (AMSR-E) SST data to the compositing process. Enhanced SST composites are produced over the ocean regions surrounding the Continental U.S. at four times each day corresponding to Terra and Aqua equator crossing times. For a given day and overpass time, both MODInd AMSR-E data from the previous seven days form a collection used in the compositing. At each MODIS pixel, cloud-free SST values from the collection are used to form a weighted average based on their latency (number of days from the current day). In this way, recent SST data are given more weight than older data. One of the primary issues involved in incorporating the AMSR-E microwave data in the composites is the tradeoff between the decreased spatial resolution of the AMSR-E data (25 km) and the increased coverage due to its near all-weather capability. Currently, the AMSR-E is given a weight of 20% compared to MODIS data, thereby preserving the spatial structure observed in the MODIS data. Day-time (night-time) AMSR-E SST data from Aqua are used with both Terra and Aqua MODIS day-time (night-time) SST data sets.

Influence of bulk microphysics schemes upon Weather Research and Forecasting (WRF) version 3.6.1 nor'easter simulations

NASA Astrophysics Data System (ADS)

Nicholls, Stephen D.; Decker, Steven G.; Tao, Wei-Kuo; Lang, Stephen E.; Shi, Jainn J.; Mohr, Karen I.

2017-03-01

This study evaluated the impact of five single- or double-moment bulk microphysics schemes (BMPSs) on Weather Research and Forecasting model (WRF) simulations of seven intense wintertime cyclones impacting the mid-Atlantic United States; 5-day long WRF simulations were initialized roughly 24 h prior to the onset of coastal cyclogenesis off the North Carolina coastline. In all, 35 model simulations (five BMPSs and seven cases) were run and their associated microphysics-related storm properties (hydrometer mixing ratios, precipitation, and radar reflectivity) were evaluated against model analysis and available gridded radar and ground-based precipitation products. Inter-BMPS comparisons of column-integrated mixing ratios and mixing ratio profiles reveal little variability in non-frozen hydrometeor species due to their shared programming heritage, yet their assumptions concerning snow and graupel intercepts, ice supersaturation, snow and graupel density maps, and terminal velocities led to considerable variability in both simulated frozen hydrometeor species and radar reflectivity. WRF-simulated precipitation fields exhibit minor spatiotemporal variability amongst BMPSs, yet their spatial extent is largely conserved. Compared to ground-based precipitation data, WRF simulations demonstrate low-to-moderate (0.217-0.414) threat scores and a rainfall distribution shifted toward higher values. Finally, an analysis of WRF and gridded radar reflectivity data via contoured frequency with altitude diagrams (CFADs) reveals notable variability amongst BMPSs, where better performing schemes favored lower graupel mixing ratios and better underlying aggregation assumptions.

Influence of Bulk Microphysics Schemes upon Weather Research and Forecasting (WRF) Version 3.6.1 Nor'easter Simulations.

PubMed

Nicholls, Stephen D; Decker, Steven G; Tao, Wei-Kuo; Lang, Stephen E; Shi, Jainn J; Mohr, Karen I

2017-01-01

This study evaluated the impact of five, single- or double- moment bulk microphysics schemes (BMPSs) on Weather Research and Forecasting model (WRF) simulations of seven, intense winter time cyclones impacting the Mid-Atlantic United States. Five-day long WRF simulations were initialized roughly 24 hours prior to the onset of coastal cyclogenesis off the North Carolina coastline. In all, 35 model simulations (5 BMPSs and seven cases) were run and their associated microphysics-related storm properties (hydrometer mixing ratios, precipitation, and radar reflectivity) were evaluated against model analysis and available gridded radar and ground-based precipitation products. Inter-BMPS comparisons of column-integrated mixing ratios and mixing ratio profiles reveal little variability in non-frozen hydrometeor species due to their shared programming heritage, yet their assumptions concerning snow and graupel intercepts, ice supersaturation, snow and graupel density maps, and terminal velocities lead to considerable variability in both simulated frozen hydrometeor species and radar reflectivity. WRF-simulated precipitation fields exhibit minor spatio-temporal variability amongst BMPSs, yet their spatial extent is largely conserved. Compared to ground-based precipitation data, WRF-simulations demonstrate low-to-moderate (0.217-0.414) threat scores and a rainfall distribution shifted toward higher values. Finally, an analysis of WRF and gridded radar reflectivity data via contoured frequency with altitude (CFAD) diagrams reveals notable variability amongst BMPSs, where better performing schemes favored lower graupel mixing ratios and better underlying aggregation assumptions.

Influence of Bulk Microphysics Schemes upon Weather Research and Forecasting (WRF) Version 3.6.1 Nor'easter Simulations

PubMed Central

Nicholls, Stephen D.; Decker, Steven G.; Tao, Wei-Kuo; Lang, Stephen E.; Shi, Jainn J.; Mohr, Karen I.

2018-01-01

This study evaluated the impact of five, single- or double- moment bulk microphysics schemes (BMPSs) on Weather Research and Forecasting model (WRF) simulations of seven, intense winter time cyclones impacting the Mid-Atlantic United States. Five-day long WRF simulations were initialized roughly 24 hours prior to the onset of coastal cyclogenesis off the North Carolina coastline. In all, 35 model simulations (5 BMPSs and seven cases) were run and their associated microphysics-related storm properties (hydrometer mixing ratios, precipitation, and radar reflectivity) were evaluated against model analysis and available gridded radar and ground-based precipitation products. Inter-BMPS comparisons of column-integrated mixing ratios and mixing ratio profiles reveal little variability in non-frozen hydrometeor species due to their shared programming heritage, yet their assumptions concerning snow and graupel intercepts, ice supersaturation, snow and graupel density maps, and terminal velocities lead to considerable variability in both simulated frozen hydrometeor species and radar reflectivity. WRF-simulated precipitation fields exhibit minor spatio-temporal variability amongst BMPSs, yet their spatial extent is largely conserved. Compared to ground-based precipitation data, WRF-simulations demonstrate low-to-moderate (0.217–0.414) threat scores and a rainfall distribution shifted toward higher values. Finally, an analysis of WRF and gridded radar reflectivity data via contoured frequency with altitude (CFAD) diagrams reveals notable variability amongst BMPSs, where better performing schemes favored lower graupel mixing ratios and better underlying aggregation assumptions. PMID:29697705

Influence of Bulk Microphysics Schemes upon Weather Research and Forecasting (WRF) Version 3.6.1 Nor'easter Simulations

NASA Technical Reports Server (NTRS)

Nicholls, Stephen D.; Decker, Steven G.; Tao, Wei-Kuo; Lang, Stephen E.; Shi, Jainn J.; Mohr, Karen Irene

2017-01-01

This study evaluated the impact of five single- or double-moment bulk microphysics schemes (BMPSs) on Weather Research and Forecasting model (WRF) simulations of seven intense wintertime cyclones impacting the mid-Atlantic United States; 5-day long WRF simulations were initialized roughly 24 hours prior to the onset of coastal cyclogenesis off the North Carolina coastline. In all, 35 model simulations (five BMPSs and seven cases) were run and their associated microphysics-related storm properties (hydrometer mixing ratios, precipitation, and radar reflectivity) were evaluated against model analysis and available gridded radar and ground-based precipitation products. Inter-BMPS comparisons of column-integrated mixing ratios and mixing ratio profiles reveal little variability in non-frozen hydrometeor species due to their shared programming heritage, yet their assumptions concerning snow and graupel intercepts, ice supersaturation, snow and graupel density maps, and terminal velocities led to considerable variability in both simulated frozen hydrometeor species and radar reflectivity. WRF-simulated precipitation fields exhibit minor spatiotemporal variability amongst BMPSs, yet their spatial extent is largely conserved. Compared to ground-based precipitation data, WRF simulations demonstrate low-to-moderate (0.217 to 0.414) threat scores and a rainfall distribution shifted toward higher values. Finally, an analysis of WRF and gridded radar reflectivity data via contoured frequency with altitude (CFAD) diagrams reveals notable variability amongst BMPSs, where better performing schemes favored lower graupel mixing ratios and better underlying aggregation assumptions.

Sensitivity of volatile organic compounds (VOCs) and ozone to land surface processes and vegetation distributions in California

NASA Astrophysics Data System (ADS)

Zhao, C.; Huang, M.; Fast, J. D.; Berg, L. K.; Qian, Y.; Guenther, A. B.; Gu, D.; Shrivastava, M. B.; Liu, Y.; Walters, S.; Jin, J.

2014-12-01

Current climate models still have large uncertainties in estimating biogenic trace gases, which can significantly affect secondary organic aerosol (SOA) formation and ultimately aerosol radiative forcing. These uncertainties result from many factors, including coupling strategy between biogenic emissions and land-surface schemes and specification of vegetation types, both of which can affect the simulated near-surface fluxes of biogenic volatile organic compounds (VOCs). In this study, sensitivity experiments are conducted using the Weather Research and Forecasting model with chemistry (WRF-Chem) to examine the sensitivity of simulated VOCs and ozone to land surface processes and vegetation distributions in California. The measurements collected during the California Nexus of Air Quality and Climate Experiment (CalNex) and the Carbonaceous Aerosol and Radiative Effects Study (CARES) conducted during May and June of 2010 provide a good opportunity to evaluate the simulations. First, the biogenic VOC emissions in the WRF-Chem simulations with the two land surface schemes, Noah and CLM4, are estimated by the Model of Emissions of Gases and Aerosols from Nature version one (MEGANv1), which has been publicly released and widely used with WRF-Chem. The impacts of land surface processes on estimating biogenic VOC emissions and simulating VOCs and ozone are investigated. Second, in this study, a newer version of MEGAN (MEGANv2.1) is coupled with CLM4 as part of WRF-Chem to examine the sensitivity of biogenic VOC emissions to the MEGAN schemes used and determine the importance of using a consistent vegetation map between a land surface scheme and the biogenic VOC emission scheme. Specifically, MEGANv2.1 is embedded into the CLM4 scheme and shares a consistent vegetation map for estimating biogenic VOC emissions. This is unlike MEGANv1 in WRF-Chem that uses a standalone vegetation map that differs from what is used in land surface schemes. Furthermore, we examine the impact of vegetation distribution on simulating VOCs and ozone by comparing coupled WRF-Chem-CLM-MEGANv2.1 simulations using multiple vegetation maps.

Intercomparison of Streamflow Simulations between WRF-Hydro and Hydrology Laboratory-Research Distributed Hydrologic Model Frameworks

NASA Astrophysics Data System (ADS)

KIM, J.; Smith, M. B.; Koren, V.; Salas, F.; Cui, Z.; Johnson, D.

2017-12-01

The National Oceanic and Atmospheric Administration (NOAA)-National Weather Service (NWS) developed the Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) framework as an initial step towards spatially distributed modeling at River Forecast Centers (RFCs). Recently, the NOAA/NWS worked with the National Center for Atmospheric Research (NCAR) to implement the National Water Model (NWM) for nationally-consistent water resources prediction. The NWM is based on the WRF-Hydro framework and is run at a 1km spatial resolution and 1-hour time step over the contiguous United States (CONUS) and contributing areas in Canada and Mexico. In this study, we compare streamflow simulations from HL-RDHM and WRF-Hydro to observations from 279 USGS stations. For streamflow simulations, HL-RDHM is run on 4km grids with the temporal resolution of 1 hour for a 5-year period (Water Years 2008-2012), using a priori parameters provided by NOAA-NWS. The WRF-Hydro streamflow simulations for the same time period are extracted from NCAR's 23 retrospective run of the NWM (version 1.0) over CONUS based on 1km grids. We choose 279 USGS stations which are relatively less affected by dams or reservoirs, in the domains of six different RFCs. We use the daily average values of simulations and observations for the convenience of comparison. The main purpose of this research is to evaluate how HL-RDHM and WRF-Hydro perform at USGS gauge stations. We compare daily time-series of observations and both simulations, and calculate the error values using a variety of error functions. Using these plots and error values, we evaluate the performances of HL-RDHM and WRF-Hydro models. Our results show a mix of model performance across geographic regions.

A New WRF-Chem Treatment for Studying Regional Scale Impacts of Cloud-Aerosol Interactions in Parameterized Cumuli

DOE Office of Scientific and Technical Information (OSTI.GOV)

Berg, Larry K.; Shrivastava, ManishKumar B.; Easter, Richard C.

A new treatment of cloud-aerosol interactions within parameterized shallow and deep convection has been implemented in WRF-Chem that can be used to better understand the aerosol lifecycle over regional to synoptic scales. The modifications to the model to represent cloud-aerosol interactions include treatment of the cloud dropletnumber mixing ratio; key cloud microphysical and macrophysical parameters (including the updraft fractional area, updraft and downdraft mass fluxes, and entrainment) averaged over the population of shallow clouds, or a single deep convective cloud; and vertical transport, activation/resuspension, aqueous chemistry, and wet removal of aerosol and trace gases in warm clouds. Thesechanges have beenmore » implemented in both the WRF-Chem chemistry packages as well as the Kain-Fritsch cumulus parameterization that has been modified to better represent shallow convective clouds. Preliminary testing of the modified WRF-Chem has been completed using observations from the Cumulus Humilis Aerosol Processing Study (CHAPS) as well as a high-resolution simulation that does not include parameterized convection. The simulation results are used to investigate the impact of cloud-aerosol interactions on the regional scale transport of black carbon (BC), organic aerosol (OA), and sulfate aerosol. Based on the simulations presented here, changes in the column integrated BC can be as large as -50% when cloud-aerosol interactions are considered (due largely to wet removal), or as large as +35% for sulfate in non-precipitating conditions due to the sulfate production in the parameterized clouds. The modifications to WRF-Chem version 3.2.1 are found to account for changes in the cloud drop number concentration (CDNC) and changes in the chemical composition of cloud-drop residuals in a way that is consistent with observations collected during CHAPS. Efforts are currently underway to port the changes described here to WRF-Chem version 3.5, and it is anticipated that they will be included in a future public release of WRF-Chem.« less

Development and application of a regional-scale atmospheric mercury model based on WRF/Chem: a Mediterranean area investigation.

PubMed

Gencarelli, Christian Natale; De Simone, Francesco; Hedgecock, Ian Michael; Sprovieri, Francesca; Pirrone, Nicola

2014-03-01

The emission, transport, deposition and eventual fate of mercury (Hg) in the Mediterranean area has been studied using a modified version of the Weather Research and Forecasting model coupled with Chemistry (WRF/Chem). This model version has been developed specifically with the aim to simulate the atmospheric processes determining atmospheric Hg emissions, concentrations and deposition online at high spatial resolution. For this purpose, the gas phase chemistry of Hg and a parametrised representation of atmospheric Hg aqueous chemistry have been added to the regional acid deposition model version 2 chemical mechanism in WRF/Chem. Anthropogenic mercury emissions from the Arctic Monitoring and Assessment Programme included in the emissions preprocessor, mercury evasion from the sea surface and Hg released from biomass burning have also been included. Dry and wet deposition processes for Hg have been implemented. The model has been tested for the whole of 2009 using measurements of total gaseous mercury from the European Monitoring and Evaluation Programme monitoring network. Speciated measurement data of atmospheric elemental Hg, gaseous oxidised Hg and Hg associated with particulate matter, from a Mediterranean oceanographic campaign (June 2009), has permitted the model's ability to simulate the atmospheric redox chemistry of Hg to be assessed. The model results highlight the importance of both the boundary conditions employed and the accuracy of the mercury speciation in the emission database. The model has permitted the reevaluation of the deposition to, and the emission from, the Mediterranean Sea. In light of the well-known high concentrations of methylmercury in a number of Mediterranean fish species, this information is important in establishing the mass balance of Hg for the Mediterranean Sea. The model results support the idea that the Mediterranean Sea is a net source of Hg to the atmosphere and suggest that the net flux is ≈30 Mg year(-1) of elemental Hg.

Multi-year application of WRF-CAM5 over East Asia-Part I: Comprehensive evaluation and formation regimes of O 3 and PM 2.5

DOE Office of Scientific and Technical Information (OSTI.GOV)

He, Jian; Zhang, Yang; Wang, Kai

Accurate simulations of air quality and climate require robust model parameterizations on regional and global scales. The Weather Research and Forecasting model with Chemistry version 3.4.1 has been coupled with physics packages from the Community Atmosphere Model version 5 (CAM5) (WRF-CAM5) to assess the robustness of the CAM5 physics package for regional modeling at higher grid resolutions than typical grid resolutions used in global modeling. In this two-part study, Part I describes the application and evaluation of WRF-CAM5 over East Asia at a horizontal resolution of 36-km for six years: 2001, 2005, 2006, 2008, 2010, and 2011. The simulations aremore » evaluated comprehensively with a variety of datasets from surface networks, satellites, and aircraft. The results show that meteorology is relatively well simulated by WRF-CAM5. However, cloud variables are largely or moderately underpredicted, indicating uncertainties in the model treatments of dynamics, thermodynamics, and microphysics of clouds/ices as well as aerosol-cloud interactions. For chemical predictions, the tropospheric column abundances of CO, NO2, and O3 are well simulated, but those of SO2 and HCHO are moderately overpredicted, and the column HCHO/NO2 indicator is underpredicted. Large biases exist in the surface concentrations of CO, NO2, and PM10 due to uncertainties in the emissions as well as vertical mixing. The underpredictions of NO lead to insufficient O3 titration, thus O3 overpredictions. The model can generally reproduce the observed O3 and PM indicators. These indicators suggest to control NOx emissions throughout the year, and VOCs emissions in summer in big cities and in winter over North China Plain, North/South Korea, and Japan to reduce surface O3, and to control SO2, NH3, and NOx throughout the year to reduce inorganic surface PM.« less

Contribution of lateral terrestrial water flows to the regional hydrological cycle: A joint soil-atmospheric moisture tagging procedure with WRF-Hydro

NASA Astrophysics Data System (ADS)

Arnault, Joel; Wei, Jianhui; Zhang, Zhenyu; Wagner, Sven; Kunstmann, Harald

2017-04-01

Water resources management requires an accurate knowledge of the behavior of the regional hydrological cycle components, including precipitation, evapotranspiration, river discharge and soil water storage. Atmospheric models such as the Weather Research and Forecasting (WRF) model provide a tool to evaluate these components. The main drawback of these atmospheric models, however, is that the terrestrial segment of the hydrological cycle is reduced to vertical infiltration, and that lateral terrestrial water flows are neglected. Recent model developments have focused on coupled atmospheric-hydrological modeling systems, such as WRF-hydro, in order to take into account subsurface, overland and river flow. The aim of this study is to investigate the contribution of lateral terrestrial water flows to the regional hydrological cycle, with the help of a joint soil-atmospheric moisture tagging procedure. This procedure is the extended version of an existing atmospheric moisture tagging method developed in WRF and WRF-Hydro (Arnault et al. 2017). It is used to quantify the partitioning of precipitation into water stored in the soil, runoff, evapotranspiration, and potentially subsequent precipitation through regional recycling. An application to a high precipitation event on 23 June 2009 in the upper Danube river basin, Germany and Austria, is presented. Precipitating water during this day is tagged for the period 2009-2011. Its contribution to runoff and evapotranspiration decreases with time, but is still not negligible in the summer 2011. At the end of the study period, less than 5 % of the precipitating water on 23 June 2009 remains in the soil. The additionally resolved lateral terrestrial water flows in WRF-Hydro modify the partitioning between surface and underground runoff, in association with a slight increase of evapotranspiration and recycled precipitation. Reference: Arnault, J., R. Knoche, J. Wei, and H. Kunstmann (2016), Evaporation tagging and atmospheric water budget analysis with WRF: A regional precipitation recycling study for West Africa, Water Resour. Res., 52, 1544-1567, doi:10.1002/2015WR017704.

Real-Time Kennedy Space Center and Cape Canaveral Air Force Station High-Resolution Model Implementation and Verification

NASA Technical Reports Server (NTRS)

Shafer, Jaclyn; Watson, Leela R.

2015-01-01

NASA's Launch Services Program, Ground Systems Development and Operations, Space Launch System and other programs at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) use the daily and weekly weather forecasts issued by the 45th Weather Squadron (45 WS) as decision tools for their day-to-day and launch operations on the Eastern Range (ER). Examples include determining if they need to limit activities such as vehicle transport to the launch pad, protect people, structures or exposed launch vehicles given a threat of severe weather, or reschedule other critical operations. The 45 WS uses numerical weather prediction models as a guide for these weather forecasts, particularly the Air Force Weather Agency (AFWA) 1.67 km Weather Research and Forecasting (WRF) model. Considering the 45 WS forecasters' and Launch Weather Officers' (LWO) extensive use of the AFWA model, the 45 WS proposed a task at the September 2013 Applied Meteorology Unit (AMU) Tasking Meeting requesting the AMU verify this model. Due to the lack of archived model data available from AFWA, verification is not yet possible. Instead, the AMU proposed to implement and verify the performance of an ER version of the high-resolution WRF Environmental Modeling System (EMS) model configured by the AMU (Watson 2013) in real time. Implementing a real-time version of the ER WRF-EMS would generate a larger database of model output than in the previous AMU task for determining model performance, and allows the AMU more control over and access to the model output archive. The tasking group agreed to this proposal; therefore the AMU implemented the WRF-EMS model on the second of two NASA AMU modeling clusters. The AMU also calculated verification statistics to determine model performance compared to observational data. Finally, the AMU made the model output available on the AMU Advanced Weather Interactive Processing System II (AWIPS II) servers, which allows the 45 WS and AMU staff to customize the model output display on the AMU and Range Weather Operations (RWO) AWIPS II client computers and conduct real-time subjective analyses.

Assessment of the Weather Research and Forecasting (WRF) model for simulation of extreme rainfall events in the upper Ganga Basin

NASA Astrophysics Data System (ADS)

Chawla, Ila; Osuri, Krishna K.; Mujumdar, Pradeep P.; Niyogi, Dev

2018-02-01

Reliable estimates of extreme rainfall events are necessary for an accurate prediction of floods. Most of the global rainfall products are available at a coarse resolution, rendering them less desirable for extreme rainfall analysis. Therefore, regional mesoscale models such as the advanced research version of the Weather Research and Forecasting (WRF) model are often used to provide rainfall estimates at fine grid spacing. Modelling heavy rainfall events is an enduring challenge, as such events depend on multi-scale interactions, and the model configurations such as grid spacing, physical parameterization and initialization. With this background, the WRF model is implemented in this study to investigate the impact of different processes on extreme rainfall simulation, by considering a representative event that occurred during 15-18 June 2013 over the Ganga Basin in India, which is located at the foothills of the Himalayas. This event is simulated with ensembles involving four different microphysics (MP), two cumulus (CU) parameterizations, two planetary boundary layers (PBLs) and two land surface physics options, as well as different resolutions (grid spacing) within the WRF model. The simulated rainfall is evaluated against the observations from 18 rain gauges and the Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) 3B42RT version 7 data. From the analysis, it should be noted that the choice of MP scheme influences the spatial pattern of rainfall, while the choice of PBL and CU parameterizations influences the magnitude of rainfall in the model simulations. Further, the WRF run with Goddard MP, Mellor-Yamada-Janjic PBL and Betts-Miller-Janjic CU scheme is found to perform best in simulating this heavy rain event. The selected configuration is evaluated for several heavy to extremely heavy rainfall events that occurred across different months of the monsoon season in the region. The model performance improved through incorporation of detailed land surface processes involving prognostic soil moisture evolution in Noah scheme compared to the simple Slab model. To analyse the effect of model grid spacing, two sets of downscaling ratios - (i) 1 : 3, global to regional (G2R) scale and (ii) 1 : 9, global to convection-permitting scale (G2C) - are employed. Results indicate that a higher downscaling ratio (G2C) causes higher variability and consequently large errors in the simulations. Therefore, G2R is adopted as a suitable choice for simulating heavy rainfall event in the present case study. Further, the WRF-simulated rainfall is found to exhibit less bias when compared with the NCEP FiNaL (FNL) reanalysis data.

Noah-MP-Crop: Enhancing cropland representation in the community land surface modeling system

NASA Astrophysics Data System (ADS)

Liu, X.; Chen, F.; Barlage, M. J.; Zhou, G.; Niyogi, D.

2015-12-01

Croplands are important in land-atmosphere interactions and in modifying local and regional weather and climate. Despite their importance, croplands are poorly represented in the current version of the coupled Weather Research and Forecasting (WRF)/ Noah land-surface modeling system, resulting in significant surface temperature and humidity biases across agriculture- dominated regions of the United States. This study aims to improve the WRF weather forecasting and regional climate simulations during the crop growing season by enhancing the representation of cropland in the Noah-MP land model. We introduced dynamic crop growth parameterization into Noah-MP and evaluated the enhanced model (Noah-MP-Crop) at both the field and regional scales with multiple crop biomass datasets, surface fluxes and soil moisture/temperature observations. We also integrated a detailed cropland cover map into WRF, enabling the model to simulate corn and soybean field across the U.S. Great Plains. Results show marked improvement in the Noah-MP-Crop performance in simulating leaf area index (LAI), crop biomass, soil temperature, and surface fluxes. Enhanced cropland representation is not only crucial for improving weather forecasting but can also help assess potential impacts of weather variability on regional hydrometeorology and crop yields. In addition to its applications to WRF, Noah-MP-Crop can be applied in high-spatial-resolution regional crop yield modeling and drought assessments

Modeling changes in extreme snowfall events in the Central Rocky Mountains Region with the Fully-Coupled WRF-Hydro Modeling System

NASA Astrophysics Data System (ADS)

gochis, David; rasmussen, Roy; Yu, Wei; Ikeda, Kyoko

2014-05-01

Modeling of extreme weather events often require very finely resolved treatment of atmospheric circulation structures in order to produce and localize large magnitudes of moisture fluxes that result in extreme precipitation. This is particularly true for cool season orographic precipitation processes where the representation of landform can significantly influence vertical velocity profiles and cloud moisture entrainment rates. In this work we report on recent progress in high resolution regional climate modeling of the Colorado Headwaters region using an updated version of the Weather Research and Forecasting (WRF) model and a hydrological extension package called WRF-Hydro. Previous work has shown that the WRF-Hydro modeling system forced by high resolution WRF model output can produce credible depictions of winter orographic precipitation and resultant monthly and annual river flows. Here we present results from a detailed study of an extreme springtime snowfall event that occurred along the Colorado Front Range in March of 2003. First an analysis of the simulated streamflows resulting from the melt out of that event are presented followed by an analysis of projected streamflows from the event where the atmospheric forcing in the WRF model is perturbed using the Psuedo-Global-Warming (PGW) perturbation methodology. Results from the impact of warming on total precipitation, snow-rain partitioning and surface hydrological fluxes (evapotranspiration and runoff) will be discussed in the context of how potential changes in temperature impact the amount of precipitation, the phase of precipitation (rain vs. snow) and the timing and amplitude of streamflow responses. It is shown that under the assumptions of the PGW method, intense precipitation rates increase during the event and, more importantly, that more precipitation falls as rain versus snow which significantly amplifies the runoff response from one where runoff is produced gradually to where runoff is more rapidly translated into streamflow values that approach significant flooding risks.

An Online Approach for Training International Climate Scientists to Use Computer Models

NASA Astrophysics Data System (ADS)

Yarker, M. B.; Mesquita, M. D.; Veldore, V.

2013-12-01

With the mounting evidence by the work of IPCC (2007), climate change has been acknowledged as a significant challenge to Sustainable Development by the international community. It is important that scientists in developing countries have access to knowledge and tools so that well-informed decisions can be made about the mitigation and adaptation of climate change. However, training researchers to use climate modeling techniques and data analysis has become a challenge, because current capacity building approaches train researchers to use climate models through short-term workshops, which requires a large amount of funding. It has also been observed that many participants who recently completed capacity building courses still view climate and weather models as a metaphorical 'black box', where data goes in and results comes out; and there is evidence that these participants lack a basic understanding of the climate system. Both of these issues limit the ability of some scientists to go beyond running a model based on rote memorization of the process. As a result, they are unable to solve problems regarding run-time errors, thus cannot determine whether or not their model simulation is reasonable. Current research in the field of science education indicates that there are effective strategies to teach learners about science models. They involve having the learner work with, experiment with, modify, and apply models in a way that is significant and informative to the learner. It has also been noted that in the case of computational models, the installation and set up process alone can be time consuming and confusing for new users, which can hinder their ability to concentrate on using, experimenting with, and applying the model to real-world scenarios. Therefore, developing an online version of capacity building is an alternative approach to the workshop training programs, which makes use of new technologies and it allows for a long-term educational process in a way that engages the learners with the subject matter, in a way that is meaningful for their region. A number of science-education courses are being conducted online within a capacity building project called 'The Future of Climate Extremes in the Caribbean (XCUBE)'. If accepted, this presentation will explore a case study related to the online training courses provided via the website m2lab.org for the XCUBE project: 'Regional Climate Modeling using WRF'. The course relates to teaching participants how to run WRF for climate simulations using a special version of the model called e-WRF (WRF for Educational purposes). This version of WRF does not require installation so that student learning can be focused on using the model itself. In order to explore the effectiveness of the course, data will be collected from the participants as they complete it. There are currently over 200 participants registered for the course and are made up of graduate students, professors, and researchers from many different science fields. Preliminary results indicate that many students enrolled in this course have previously taken a WRF tutorial, but do not feel confident enough to use it. Despite having taken a tutorial previously, for some participants the basic design of the model was a new concept to them. If accepted, a statistical analysis will be performed as more students complete the course.

Evaluation of quality of precipitation products: A case study using WRF and IMERG data over the central United States

NASA Astrophysics Data System (ADS)

Zhang, J.; Lin, L. F.; Bras, R. L.

2017-12-01

Hydrological applications rely on the availability and quality of precipitation products, specially model- and satellite-based products for use in areas without ground measurements. It is known that the quality of model- and satellite-based precipitation products are complementary—model-based products exhibiting high quality during winters while satellite-based products seem to be better during summers. To explore that behavior, this study uses 2-m air temperature as auxiliary information to evaluate high-resolution (0.1°×0.1° every hour) precipitation products from Weather Research and Forecasting (WRF) simulations and from version-4 Integrated Multi-satellite Retrievals for GPM (IMERG) early and final runs. The products are evaluated relative to the reference NCEP Stage IV precipitation estimates over the central United States in 2016. The results show that the WRF and IMERG final-run estimates are nearly unbiased while the IMERG early-run estimates positively biased. The results also show that the WRF estimates exhibit high correlations with the reference data when the temperature falls below 280°K and the IMERG estimates (i.e., both early and final runs) do so when the temperature exceeds 280°K. Moreover, the temperature threshold of 280°K, which distinguishes the quality of the WRF and the IMERG products, does not vary significantly with either season or location. This study not only adds insight into current precipitation research on the quality of precipitation products but also suggests a simple way for choosing either a model- or satellite-based product or a hybrid model/satellite product for applications.

A Portable Regional Weather and Climate Downscaling System Using GEOS-5, LIS-6, WRF, and the NASA Workflow Tool

NASA Astrophysics Data System (ADS)

Kemp, E. M.; Putman, W. M.; Gurganus, J.; Burns, R. W.; Damon, M. R.; McConaughy, G. R.; Seablom, M. S.; Wojcik, G. S.

2009-12-01

We present a regional downscaling system (RDS) suitable for high-resolution weather and climate simulations in multiple supercomputing environments. The RDS is built on the NASA Workflow Tool, a software framework for configuring, running, and managing computer models on multiple platforms with a graphical user interface. The Workflow Tool is used to run the NASA Goddard Earth Observing System Model Version 5 (GEOS-5), a global atmospheric-ocean model for weather and climate simulations down to 1/4 degree resolution; the NASA Land Information System Version 6 (LIS-6), a land surface modeling system that can simulate soil temperature and moisture profiles; and the Weather Research and Forecasting (WRF) community model, a limited-area atmospheric model for weather and climate simulations down to 1-km resolution. The Workflow Tool allows users to customize model settings to user needs; saves and organizes simulation experiments; distributes model runs across different computer clusters (e.g., the DISCOVER cluster at Goddard Space Flight Center, the Cray CX-1 Desktop Supercomputer, etc.); and handles all file transfers and network communications (e.g., scp connections). Together, the RDS is intended to aid researchers by making simulations as easy as possible to generate on the computer resources available. Initial conditions for LIS-6 and GEOS-5 are provided by Modern Era Retrospective-Analysis for Research and Applications (MERRA) reanalysis data stored on DISCOVER. The LIS-6 is first run for 2-4 years forced by MERRA atmospheric analyses, generating initial conditions for the WRF soil physics. GEOS-5 is then initialized from MERRA data and run for the period of interest. Large-scale atmospheric data, sea-surface temperatures, and sea ice coverage from GEOS-5 are used as boundary conditions for WRF, which is run for the same period of interest. Multiply nested grids are used for both LIS-6 and WRF, with the innermost grid run at a resolution sufficient for typical local weather features (terrain, convection, etc.) All model runs, restarts, and file transfers are coordinated by the Workflow Tool. Two use cases are being pursued. First, the RDS generates regional climate simulations down to 4-km for the Chesapeake Bay region, with WRF output provided as input to more specialized models (e.g., ocean/lake, hydrological, marine biology, and air pollution). This will allow assessment of climate impact on local interests (e.g., changes in Bay water levels and temperatures, innundation, fish kills, etc.) Second, the RDS generates high-resolution hurricane simulations in the tropical North Atlantic. This use case will support Observing System Simulation Experiments (OSSEs) of dynamically-targeted lidar observations as part of the NASA Sensor Web Simulator project. Sample results will be presented at the AGU Fall Meeting.

Applicability of WRF-Lake System in Studying Reservoir-Induced Impacts on Local Climate: Case Study of Two Reservoirs with Contrasting Characteristics

NASA Astrophysics Data System (ADS)

Wang, F.; Zhu, D.; Ni, G.; Sun, T.

2017-12-01

Large reservoirs play a key role in regional hydrological cycles as well as in modulating the local climate. The emerging large reservoirs in concomitant with rapid hydropower exploitation in southwestern China warrant better understanding of their impacts on local and regional climates. One of the crucial pathways through which reservoirs impact the climate is lake-atmospheric interaction. Although such interactions have been widely studied with numeric weather prediction (NWP) models, an outstanding limitation across various NWPs resides on the poor thermodynamic representation of lakes. The recent version of Weather Research and Forecasting (WRF) system has been equipped with a one-dimensional lake model to better represent the thermodynamics of large water body and has been shown to enhance the its predication skill in the lake-atmospheric interaction. In this study, we further explore the applicability of the WRF-Lake system in two reservoirs with contrasting characteristics: Miyun Reservoir with an average depth of 30 meters in North China Plain, and Nuozhadu Reservoir with an average depth of 200 meters in the Tibetan Plateau Region. Driven by the high spatiotemporal resolution meteorological forcing data, the WRF-Lake system is used to simulate the water temperature and surface energy budgets of the two reservoirs after the evaluation against temperature observations. The simulated results show the WRF-Lake model can well predict the vertical profile of water temperature in Miyun Reservoir, but underestimates deep water temperature and overestimates surface temperature in the deeper Nuozhadu Reservoir. In addition, sensitivity analysis indicates the poor performance of the WRF-Lake system in Nuozhadu Reservoir could be attributed to the weak vertical mixing in the model, which can be improved by tuning the eddy diffusion coefficient ke . Keywords: reservoir-induced climatic impact; lake-atmospheric interaction; WRF-Lake system; hydropower exploitation

Regional Climate Modeling of Volcanic Eruptions and the Arctic Climate System: A Baffin Island Case Study

NASA Astrophysics Data System (ADS)

Losic, M.; Robock, A.

2010-12-01

It is well-understood that the effects of volcanic aerosol loading into the stratosphere are transient, with global cooling lasting only a few years after a single large eruption. Geological evidence collected from Northern Baffin Island, Canada, suggests ice cap growth began soon after a succession of several large eruptions in the 13th century, and they did not start to melt until roughly a century ago. We investigate which feedbacks allowed these ice caps to be maintained long after the transient forcing of the volcanic aerosols, by conducting sensitivity studies with the Weather Research and Forecasting (WRF) Model and Polar WRF, a version of WRF developed specifically for the polar regions. Results from an ensemble of month-long regional simulations over Baffin Island suggest that better treatment of snow and ice in Polar WRF improves our regional climate simulations. Thus, sensitivity test results from decade-long runs with imposed changes to boundary condition temperatures and carbon dioxide concentrations using Polar WRF are presented. Preliminary findings suggest that not only large scale but localized climate feedbacks play an important role in the responses of the ice caps after temperature and carbon dioxide forcings are applied. The results from these and further sensitivity tests will provide insight into the influence of regional feedbacks on the persistence of these ice caps long after the 13th century eruptions.

Improved Modeling of Land-Atmosphere Interactions using a Coupled Version of WRF with the Land Information System

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; LaCasse, Katherine M.; Santanello, Joseph A., Jr.; Lapenta, William M.; Petars-Lidard, Christa D.

2007-01-01

The exchange of energy and moisture between the Earth's surface and the atmospheric boundary layer plays a critical role in many hydrometeorological processes. Accurate and high-resolution representations of surface properties such as sea-surface temperature (SST), vegetation, soil temperature and moisture content, and ground fluxes are necessary to better understand the Earth-atmosphere interactions and improve numerical predictions of weather and climate phenomena. The NASA/NWS Short-term Prediction Research and Transition (SPORT) Center is currently investigating the potential benefits of assimilating high-resolution datasets derived from the NASA moderate resolution imaging spectroradiometer (MODIS) instruments using the Weather Research and Forecasting (WRF) model and the Goddard Space Flight Center Land Information System (LIS). The LIS is a software framework that integrates satellite and ground-based observational and modeled data along with multiple land surface models (LSMs) and advanced computing tools to accurately characterize land surface states and fluxes. The LIS can be run uncoupled to provide a high-resolution land surface initial condition, and can also be run in a coupled mode with WRF to integrate surface and soil quantities using any of the LSMs available in LIS. The LIS also includes the ability to optimize the initialization of surface and soil variables by tuning the spin-up time period and atmospheric forcing parameters, which cannot be done in the standard WRF. Among the datasets available from MODIS, a leaf-area index field and composite SST analysis are used to improve the lower boundary and initial conditions to the LIS/WRF coupled model over both land and water. Experiments will be conducted to measure the potential benefits from using the coupled LIS/WRF model over the Florida peninsula during May 2004. This month experienced relatively benign weather conditions, which will allow the experiments to focus on the local and mesoscale impacts of the high-resolution MODIS datasets and optimized soil and surface initial conditions. Follow-on experiments will examine the utility of such an optimized WRF configuration for more complex weather scenarios such as convective initiation. This paper will provide an overview of the experiment design and present preliminary results from selected cases in May 2004.

A Dynamical Downscaling study over the Great Lakes Region Using WRF-Lake: Historical Simulation

NASA Astrophysics Data System (ADS)

Xiao, C.; Lofgren, B. M.

2014-12-01

As the largest group of fresh water bodies on Earth, the Laurentian Great Lakes have significant influence on local and regional weather and climate through their unique physical features compared with the surrounding land. Due to the limited spatial resolution and computational efficiency of general circulation models (GCMs), the Great Lakes are geometrically ignored or idealized into several grid cells in GCMs. Thus, the nested regional climate modeling (RCM) technique, known as dynamical downscaling, serves as a feasible solution to fill the gap. The latest Weather Research and Forecasting model (WRF) is employed to dynamically downscale the historical simulation produced by the Geophysical Fluid Dynamics Laboratory-Coupled Model (GFDL-CM3) from 1970-2005. An updated lake scheme originated from the Community Land Model is implemented in the latest WRF version 3.6. It is a one-dimensional mass and energy balance scheme with 20-25 model layers, including up to 5 snow layers on the lake ice, 10 water layers, and 10 soil layers on the lake bottom. The lake scheme is used with actual lake points and lake depth. The preliminary results show that WRF-Lake model, with a fine horizontal resolution and realistic lake representation, provides significantly improved hydroclimates, in terms of lake surface temperature, annual cycle of precipitation, ice content, and lake-effect snowfall. Those improvements suggest that better resolution of the lakes and the mesoscale process of lake-atmosphere interaction are crucial to understanding the climate and climate change in the Great Lakes region.

Intercomparison of Martian Lower Atmosphere Simulated Using Different Planetary Boundary Layer Parameterization Schemes

NASA Technical Reports Server (NTRS)

Natarajan, Murali; Fairlie, T. Duncan; Dwyer Cianciolo, Alicia; Smith, Michael D.

2015-01-01

We use the mesoscale modeling capability of Mars Weather Research and Forecasting (MarsWRF) model to study the sensitivity of the simulated Martian lower atmosphere to differences in the parameterization of the planetary boundary layer (PBL). Characterization of the Martian atmosphere and realistic representation of processes such as mixing of tracers like dust depend on how well the model reproduces the evolution of the PBL structure. MarsWRF is based on the NCAR WRF model and it retains some of the PBL schemes available in the earth version. Published studies have examined the performance of different PBL schemes in NCAR WRF with the help of observations. Currently such assessments are not feasible for Martian atmospheric models due to lack of observations. It is of interest though to study the sensitivity of the model to PBL parameterization. Typically, for standard Martian atmospheric simulations, we have used the Medium Range Forecast (MRF) PBL scheme, which considers a correction term to the vertical gradients to incorporate nonlocal effects. For this study, we have also used two other parameterizations, a non-local closure scheme called Yonsei University (YSU) PBL scheme and a turbulent kinetic energy closure scheme called Mellor- Yamada-Janjic (MYJ) PBL scheme. We will present intercomparisons of the near surface temperature profiles, boundary layer heights, and wind obtained from the different simulations. We plan to use available temperature observations from Mini TES instrument onboard the rovers Spirit and Opportunity in evaluating the model results.

Evaluation of cool season precipitation event characteristics over the Northeast US in a suite of downscaled climate model hindcasts

NASA Astrophysics Data System (ADS)

Loikith, Paul C.; Waliser, Duane E.; Kim, Jinwon; Ferraro, Robert

2017-08-01

Cool season precipitation event characteristics are evaluated across a suite of downscaled climate models over the northeastern US. Downscaled hindcast simulations are produced by dynamically downscaling the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA2) using the National Aeronautics and Space Administration (NASA)-Unified Weather Research and Forecasting (WRF) regional climate model (RCM) and the Goddard Earth Observing System Model, Version 5 (GEOS-5) global climate model. NU-WRF RCM simulations are produced at 24, 12, and 4-km horizontal resolutions using a range of spectral nudging schemes while the MERRA2 global downscaled run is provided at 12.5-km. All model runs are evaluated using four metrics designed to capture key features of precipitation events: event frequency, event intensity, even total, and event duration. Overall, the downscaling approaches result in a reasonable representation of many of the key features of precipitation events over the region, however considerable biases exist in the magnitude of each metric. Based on this evaluation there is no clear indication that higher resolution simulations result in more realistic results in general, however many small-scale features such as orographic enhancement of precipitation are only captured at higher resolutions suggesting some added value over coarser resolution. While the differences between simulations produced using nudging and no nudging are small, there is some improvement in model fidelity when nudging is introduced, especially at a cutoff wavelength of 600 km compared to 2000 km. Based on the results of this evaluation, dynamical regional downscaling using NU-WRF results in a more realistic representation of precipitation event climatology than the global downscaling of MERRA2 using GEOS-5.

A warm-season comparison of WRF coupled to the CLM4.0, Noah-MP, and Bucket hydrology land surface schemes over the central USA

NASA Astrophysics Data System (ADS)

Van Den Broeke, Matthew S.; Kalin, Andrew; Alavez, Jose Abraham Torres; Oglesby, Robert; Hu, Qi

2017-11-01

In climate modeling studies, there is a need to choose a suitable land surface model (LSM) while adhering to available resources. In this study, the viability of three LSM options (Community Land Model version 4.0 [CLM4.0], Noah-MP, and the five-layer thermal diffusion [Bucket] scheme) in the Weather Research and Forecasting model version 3.6 (WRF3.6) was examined for the warm season in a domain centered on the central USA. Model output was compared to Parameter-elevation Relationships on Independent Slopes Model (PRISM) data, a gridded observational dataset including mean monthly temperature and total monthly precipitation. Model output temperature, precipitation, latent heat (LH) flux, sensible heat (SH) flux, and soil water content (SWC) were compared to observations from sites in the Central and Southern Great Plains region. An overall warm bias was found in CLM4.0 and Noah-MP, with a cool bias of larger magnitude in the Bucket model. These three LSMs produced similar patterns of wet and dry biases. Model output of SWC and LH/SH fluxes were compared to observations, and did not show a consistent bias. Both sophisticated LSMs appear to be viable options for simulating the effects of land use change in the central USA.

An Operational Configuration of the ARPS Data Analysis System to Initialize WRF in the NM'S Environmental Modeling System

NASA Technical Reports Server (NTRS)

Case, Jonathan; Blottman, Pete; Hoeth, Brian; Oram, Timothy

2006-01-01

The Weather Research and Forecasting (WRF) model is the next generation community mesoscale model designed to enhance collaboration between the research and operational sectors. The NM'S as a whole has begun a transition toward WRF as the mesoscale model of choice to use as a tool in making local forecasts. Currently, both the National Weather Service in Melbourne, FL (NWS MLB) and the Spaceflight Meteorology Group (SMG) are running the Advanced Regional Prediction System (AIRPS) Data Analysis System (ADAS) every 15 minutes over the Florida peninsula to produce high-resolution diagnostics supporting their daily operations. In addition, the NWS MLB and SMG have used ADAS to provide initial conditions for short-range forecasts from the ARPS numerical weather prediction (NWP) model. Both NM'S MLB and SMG have derived great benefit from the maturity of ADAS, and would like to use ADAS for providing initial conditions to WRF. In order to assist in this WRF transition effort, the Applied Meteorology Unit (AMU) was tasked to configure and implement an operational version of WRF that uses output from ADAS for the model initial conditions. Both agencies asked the AMU to develop a framework that allows the ADAS initial conditions to be incorporated into the WRF Environmental Modeling System (EMS) software. Developed by the NM'S Science Operations Officer (S00) Science and Training Resource Center (STRC), the EMS is a complete, full physics, NWP package that incorporates dynamical cores from both the National Center for Atmospheric Research's Advanced Research WRF (ARW) and the National Centers for Environmental Prediction's Non-Hydrostatic Mesoscale Model (NMM) into a single end-to-end forecasting system. The EMS performs nearly all pre- and postprocessing and can be run automatically to obtain external grid data for WRF boundary conditions, run the model, and convert the data into a format that can be readily viewed within the Advanced Weather Interactive Processing System. The EMS has also incorporated the WRF Standard Initialization (SI) graphical user interface (GUT), which allows the user to set up the domain, dynamical core, resolution, etc., with ease. In addition to the SI GUT, the EMS contains a number of configuration files with extensive documentation to help the user select the appropriate input parameters for model physics schemes, integration timesteps, etc. Therefore, because of its streamlined capability, it is quite advantageous to configure ADAS to provide initial condition data to the EMS software. One of the biggest potential benefits of configuring ADAS for ingest into the EMS is that the analyses could be used to initialize either the ARW or NMM. Currently, the ARPS/ADAS software has a conversion routine only for the ARW dynamical core. However, since the NIvIM runs about 2.5 times faster than the ARW, it is quite advantageous to be able to run an ADAS/NMM configuration operationally due to the increased efficiency.

Simulations of the Holuhraun eruption 2014 with WRF-Chem and evaluation with satellite and ground based SO2 measurements

NASA Astrophysics Data System (ADS)

Hirtl, Marcus; Arnold-Arias, Delia; Flandorfer, Claudia; Maurer, Christian; Mantovani, Simone; Natali, Stefano

2016-04-01

Volcanic eruptions, with gas or/and particle emissions, directly influence our environment, with special significance when they either occur near inhabited regions or are transported towards them. In addition to the well-known affectation of air traffic, with large economic impacts, the ground touching plumes can lead directly to an influence of soil, water and even to a decrease of air quality. The eruption of Holuhraun in August 2014 in central Iceland is the country's largest lava and gas eruption since the Lakagígar eruption in 1783. Nevertheless, very little volcanic ash was produced. The main atmospheric threat from this event was the SO2 pollution that frequently violated the Icelandic National Air Quality Standards in many population centers. However, the SO2 affectation was not limited to Iceland but extended to mainland Europe. The on-line coupled model WRF-Chem is used to simulate the dispersion of SO2 for this event that affected the central European regions. The volcanic emissions are considered in addition to the anthropogenic and biogenic ground sources at European scale. A modified version of WRF-Chem version 4.1 is used in order to use time depending injection heights and mass fluxes which were obtained from in situ observations. WRF-Chem uses complex gas- (RADM2) and aerosol- (MADE-SORGAM) chemistry and is operated on a European domain (12 km resolution), and a nested grid covering the Alpine region (4 km resolution). The study is showing the evaluation of the model simulations with satellite and ground based measurement data of SO2. The analysis is conducted on a data management platform, which is currently developed in the frame of the ESA-funded project TAMP "Technology and Atmospheric Mission Platform": it provides comprehensive functionalities to visualize and numerically compare data from different sources (model, satellite and ground-measurements).

Improving Regional Forecast by Assimilating Atmospheric InfraRed Sounder (AIRS) Profiles into WRF Model

NASA Technical Reports Server (NTRS)

Chou, Shih-Hung; Zavodsky, Brad; Jedlovec, Gary J.

2009-01-01

In data sparse regions, remotely-sensed observations can be used to improve analyses and produce improved forecasts. One such source comes from the Atmospheric InfraRed Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), represents one of the most advanced space-based atmospheric sounding systems. The purpose of this paper is to describe a procedure to optimally assimilate high resolution AIRS profile data into a regional configuration of the Advanced Research WRF (ARW) version 2.2 using WRF-Var. The paper focuses on development of background error covariances for the regional domain and background type, and an optimal methodology for ingesting AIRS temperature and moisture profiles as separate overland and overwater retrievals with different error characteristics. The AIRS thermodynamic profiles are derived from the version 5.0 Earth Observing System (EOS) science team retrieval algorithm and contain information about the quality of each temperature layer. The quality indicators were used to select the highest quality temperature and moisture data for each profile location and pressure level. The analyses were then used to conduct a month-long series of regional forecasts over the continental U.S. The long-term impacts of AIRS profiles on forecast were assessed against verifying NAM analyses and stage IV precipitation data.

Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data

NASA Astrophysics Data System (ADS)

Lee, Joseph C. Y.; Lundquist, Julie K.

2017-11-01

Forecasts of wind-power production are necessary to facilitate the integration of wind energy into power grids, and these forecasts should incorporate the impact of wind-turbine wakes. This paper focuses on a case study of four diurnal cycles with significant power production, and assesses the skill of the wind farm parameterization (WFP) distributed with the Weather Research and Forecasting (WRF) model version 3.8.1, as well as its sensitivity to model configuration. After validating the simulated ambient flow with observations, we quantify the value of the WFP as it accounts for wake impacts on power production of downwind turbines. We also illustrate with statistical significance that a vertical grid with approximately 12 m vertical resolution is necessary for reproducing the observed power production. Further, the WFP overestimates wake effects and hence underestimates downwind power production during high wind speed, highly stable, and low turbulence conditions. We also find the WFP performance is independent of the number of wind turbines per model grid cell and the upwind-downwind position of turbines. Rather, the ability of the WFP to predict power production is most dependent on the skill of the WRF model in simulating the ambient wind speed.

Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data

DOE PAGES

Lee, Joseph C. Y.; Lundquist, Julie K.

2017-11-23

Forecasts of wind-power production are necessary to facilitate the integration of wind energy into power grids, and these forecasts should incorporate the impact of wind-turbine wakes. Our paper focuses on a case study of four diurnal cycles with significant power production, and assesses the skill of the wind farm parameterization (WFP) distributed with the Weather Research and Forecasting (WRF) model version 3.8.1, as well as its sensitivity to model configuration. After validating the simulated ambient flow with observations, we quantify the value of the WFP as it accounts for wake impacts on power production of downwind turbines. We also illustratemore » with statistical significance that a vertical grid with approximately 12 m vertical resolution is necessary for reproducing the observed power production. Further, the WFP overestimates wake effects and hence underestimates downwind power production during high wind speed, highly stable, and low turbulence conditions. We also find the WFP performance is independent of the number of wind turbines per model grid cell and the upwind–downwind position of turbines. Rather, the ability of the WFP to predict power production is most dependent on the skill of the WRF model in simulating the ambient wind speed.« less

Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Joseph C. Y.; Lundquist, Julie K.

Forecasts of wind-power production are necessary to facilitate the integration of wind energy into power grids, and these forecasts should incorporate the impact of wind-turbine wakes. Our paper focuses on a case study of four diurnal cycles with significant power production, and assesses the skill of the wind farm parameterization (WFP) distributed with the Weather Research and Forecasting (WRF) model version 3.8.1, as well as its sensitivity to model configuration. After validating the simulated ambient flow with observations, we quantify the value of the WFP as it accounts for wake impacts on power production of downwind turbines. We also illustratemore » with statistical significance that a vertical grid with approximately 12 m vertical resolution is necessary for reproducing the observed power production. Further, the WFP overestimates wake effects and hence underestimates downwind power production during high wind speed, highly stable, and low turbulence conditions. We also find the WFP performance is independent of the number of wind turbines per model grid cell and the upwind–downwind position of turbines. Rather, the ability of the WFP to predict power production is most dependent on the skill of the WRF model in simulating the ambient wind speed.« less

Description and evaluation of the Community Multiscale Air ...

EPA Pesticide Factsheets

The Community Multiscale Air Quality (CMAQ) model is a comprehensive multipollutant air quality modeling system developed and maintained by the US Environmental Protection Agency's (EPA) Office of Research and Development (ORD). Recently, version 5.1 of the CMAQ model (v5.1) was released to the public, incorporating a large number of science updates and extended capabilities over the previous release version of the model (v5.0.2). These updates include the following: improvements in the meteorological calculations in both CMAQ and the Weather Research and Forecast (WRF) model used to provide meteorological fields to CMAQ, updates to the gas and aerosol chemistry, revisions to the calculations of clouds and photolysis, and improvements to the dry and wet deposition in the model. Sensitivity simulations isolating several of the major updates to the modeling system show that changes to the meteorological calculations result in enhanced afternoon and early evening mixing in the model, periods when the model historically underestimates mixing. This enhanced mixing results in higher ozone (O3) mixing ratios on average due to reduced NO titration, and lower fine particulate matter (PM2. 5) concentrations due to greater dilution of primary pollutants (e.g., elemental and organic carbon). Updates to the clouds and photolysis calculations greatly improve consistency between the WRF and CMAQ models and result in generally higher O3 mixing ratios, primarily due to reduced

Evaluation of a multi-scale WRF-CAM5 simulation during the 2010 East Asian Summer Monsoon

DOE PAGES

Campbell, Patrick; Zhang, Yang; Wang, Kai; ...

2017-09-08

The Weather Research and Forecasting model with Chemistry (WRF-Chem) with the physics package of the Community Atmosphere Model Version 5 (CAM5) has been applied at multiple scales over Eastern China (EC) and the Yangtze River Delta (YRD) to evaluate how increased horizontal resolution with physics designed for a coarser resolution climate model impacts aerosols and clouds, and the resulting precipitation characteristics and performance during the 2010 East Asian Summer Monsoon (EASM). Despite large underpredictions in surface aerosol concentrations and aerosol optical depth, there is good spatial agreement with surface observations of chemical predictions, and increasing spatial resolution tends to improvemore » performance. Model bias and normalized root mean square values for precipitation predictions are relatively small, but there are significant differences when comparing modeled and observed probability density functions for precipitation in EC and YRD. Increasing model horizontal resolution tends to reduce model bias and error for precipitation predictions. The surface and column aerosol loading is maximized between about 32°N and 42°N in early to mid-May during the 2010 EASM, and then shifts north while decreasing in magnitude during July and August. Changing model resolution moderately changes the spatiotemporal relationships between aerosols, cloud properties, and precipitation during the EASM, thus demonstrating the importance of model grid resolution in simulating EASM circulation and rainfall patterns over EC and the YRD. In conclusion, results from this work demonstrate the capability and limitations in the aerosol, cloud, and precipitation representation of WRF-CAM5 for regional-scale applications down to relatively fine horizontal resolutions. Further WRF-CAM5 model development and application in this area is needed.« less

Evaluation of a multi-scale WRF-CAM5 simulation during the 2010 East Asian Summer Monsoon

DOE Office of Scientific and Technical Information (OSTI.GOV)

Campbell, Patrick; Zhang, Yang; Wang, Kai

The Weather Research and Forecasting model with Chemistry (WRF-Chem) with the physics package of the Community Atmosphere Model Version 5 (CAM5) has been applied at multiple scales over Eastern China (EC) and the Yangtze River Delta (YRD) to evaluate how increased horizontal resolution with physics designed for a coarser resolution climate model impacts aerosols and clouds, and the resulting precipitation characteristics and performance during the 2010 East Asian Summer Monsoon (EASM). Despite large underpredictions in surface aerosol concentrations and aerosol optical depth, there is good spatial agreement with surface observations of chemical predictions, and increasing spatial resolution tends to improvemore » performance. Model bias and normalized root mean square values for precipitation predictions are relatively small, but there are significant differences when comparing modeled and observed probability density functions for precipitation in EC and YRD. Increasing model horizontal resolution tends to reduce model bias and error for precipitation predictions. The surface and column aerosol loading is maximized between about 32N and 42N in early to mid-May during the 2010 EASM, and then shifts north while decreasing in magnitude during July and August. Changing model resolution moderately changes the spatiotemporal relationships between aerosols, cloud properties, and precipitation during the EASM, thus demonstrating the importance of model grid resolution in simulating EASM circulation and rainfall patterns over EC and the YRD. Results from this work demonstrate the capability and limitations in the aerosol, cloud, and precipitation representation of WRF-CAM5 for regional-scale applications down to relatively fine horizontal resolutions. Further WRF-CAM5 model development and application in this area is needed.« less

Evaluation of a multi-scale WRF-CAM5 simulation during the 2010 East Asian Summer Monsoon

DOE Office of Scientific and Technical Information (OSTI.GOV)

Campbell, Patrick; Zhang, Yang; Wang, Kai

The Weather Research and Forecasting model with Chemistry (WRF-Chem) with the physics package of the Community Atmosphere Model Version 5 (CAM5) has been applied at multiple scales over Eastern China (EC) and the Yangtze River Delta (YRD) to evaluate how increased horizontal resolution with physics designed for a coarser resolution climate model impacts aerosols and clouds, and the resulting precipitation characteristics and performance during the 2010 East Asian Summer Monsoon (EASM). Despite large underpredictions in surface aerosol concentrations and aerosol optical depth, there is good spatial agreement with surface observations of chemical predictions, and increasing spatial resolution tends to improvemore » performance. Model bias and normalized root mean square values for precipitation predictions are relatively small, but there are significant differences when comparing modeled and observed probability density functions for precipitation in EC and YRD. Increasing model horizontal resolution tends to reduce model bias and error for precipitation predictions. The surface and column aerosol loading is maximized between about 32°N and 42°N in early to mid-May during the 2010 EASM, and then shifts north while decreasing in magnitude during July and August. Changing model resolution moderately changes the spatiotemporal relationships between aerosols, cloud properties, and precipitation during the EASM, thus demonstrating the importance of model grid resolution in simulating EASM circulation and rainfall patterns over EC and the YRD. In conclusion, results from this work demonstrate the capability and limitations in the aerosol, cloud, and precipitation representation of WRF-CAM5 for regional-scale applications down to relatively fine horizontal resolutions. Further WRF-CAM5 model development and application in this area is needed.« less

Investigation of a long time series of CO2 from a tall tower using WRF-SPA

NASA Astrophysics Data System (ADS)

Smallman, Luke; Williams, Mathew; Moncrieff, John B.

2013-04-01

Atmospheric observations from tall towers are an important source of information about CO2 exchange at the regional scale. Here, we have used a forward running model, WRF-SPA, to generate a time series of CO2 at a tall tower for comparison with observations from Scotland over multiple years (2006-2008). We use this comparison to infer strength and distribution of sources and sinks of carbon and ecosystem process information at the seasonal scale. The specific aim of this research is to combine a high resolution (6 km) forward running meteorological model (WRF) with a modified version of a mechanistic ecosystem model (SPA). SPA provides surface fluxes calculated from coupled energy, hydrological and carbon cycles. This closely coupled representation of the biosphere provides realistic surface exchanges to drive mixing within the planetary boundary layer. The combined model is used to investigate the sources and sinks of CO2 and to explore which land surfaces contribute to a time series of hourly observations of atmospheric CO2 at a tall tower, Angus, Scotland. In addition to comparing the modelled CO2 time series to observations, modelled ecosystem specific (i.e. forest, cropland, grassland) CO2 tracers (e.g., assimilation and respiration) have been compared to the modelled land surface assimilation to investigate how representative tall tower observations are of land surface processes. WRF-SPA modelled CO2 time series compares well to observations (R2 = 0.67, rmse = 3.4 ppm, bias = 0.58 ppm). Through comparison of model-observation residuals, we have found evidence that non-cropped components of agricultural land (e.g., hedgerows and forest patches) likely contribute a significant and observable impact on regional carbon balance.

North Pacific Mesoscale Coupled Air-Ocean Simulations Compared with Observations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Koracin, Darko; Cerovecki, Ivana; Vellore, Ramesh

2013-04-11

Executive summary The main objective of the study was to investigate atmospheric and ocean interaction processes in the western Pacific and, in particular, effects of significant ocean heat loss in the Kuroshio and Kuroshio Extension regions on the lower and upper atmosphere. It is yet to be determined how significant are these processes are on climate scales. The understanding of these processes led us also to development of the methodology of coupling the Weather and Research Forecasting model with the Parallel Ocean Program model for western Pacific regional weather and climate simulations. We tested NCAR-developed research software Coupler 7 formore » coupling of the WRF and POP models and assessed its usability for regional-scale applications. We completed test simulations using the Coupler 7 framework, but implemented a standard WRF model code with options for both one- and two-way mode coupling. This type of coupling will allow us to seamlessly incorporate new WRF updates and versions in the future. We also performed a long-term WRF simulation (15 years) covering the entire North Pacific as well as high-resolution simulations of a case study which included extreme ocean heat losses in the Kuroshio and Kuroshio Extension regions. Since the extreme ocean heat loss occurs during winter cold air outbreaks (CAO), we simulated and analyzed a case study of a severe CAO event in January 2000 in detail. We found that the ocean heat loss induced by CAOs is amplified by additional advection from mesocyclones forming on the southern part of the Japan Sea. Large scale synoptic patterns with anomalously strong anticyclone over Siberia and Mongolia, deep Aleutian Low, and the Pacific subtropical ridge are a crucial setup for the CAO. It was found that the onset of the CAO is related to the breaking of atmospheric Rossby waves and vertical transport of vorticity that facilitates meridional advection. The study also indicates that intrinsic parameterization of the surface fluxes within the WRF model needs more evaluation and analysis.« less

Gaseous Chemistry and Aerosol Mechanism Developments for Version 3.5.1 of the Online Regional Model, WRF-Chem

DOE Office of Scientific and Technical Information (OSTI.GOV)

Archer-Nicholls, Scott; Lowe, Douglas; Utembe, Steve

We have made a number of developments in the regional coupled model WRF-Chem, with the aim of making the model more suitable for prediction of atmospheric composition and of interactions between air quality and weather. We have worked on the European domain, with a particular focus on making the model suitable for the study of night time chemistry and oxidation by the nitrate radical in the UK atmosphere. A reduced form of the Common Reactive Intermediates gas-phase chemical mechanism (CRIv2-R5) has been implemented to enable more explicit simulation of VOC degradation. N2O5 heterogeneous chemistry has been added to the existingmore » sectional MOSAIC aerosol module, and coupled to both the CRIv2-R5 and existing CBM-Z gas phase scheme. Modifications have also been made to the sea-spray aerosol emission representation, allowing the inclusion of primary organic material in sea-spray aerosol. Driven by appropriate emissions, wind fields and chemical boundary conditions, implementation of the different developments is illustrated in order to demonstrate the impact that these changes have in the North-West European domain. These developments are now part of the freely available WRF-Chem distribution.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Berg, Larry K.; Gustafson, William I.; Kassianov, Evgueni I.

A new treatment for shallow clouds has been introduced into the Weather Research and Forecasting (WRF) model. The new scheme, called the cumulus potential (CuP) scheme, replaces the ad-hoc trigger function used in the Kain-Fritsch cumulus parameterization with a trigger function related to the distribution of temperature and humidity in the convective boundary layer via probability density functions (PDFs). An additional modification to the default version of WRF is the computation of a cumulus cloud fraction based on the time scales relevant for shallow cumuli. Results from three case studies over the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM)more » site in north central Oklahoma are presented. These days were selected because of the presence of shallow cumuli over the ARM site. The modified version of WRF does a much better job predicting the cloud fraction and the downwelling shortwave irradiance thancontrol simulations utilizing the default Kain-Fritsch scheme. The modified scheme includes a number of additional free parameters, including the number and size of bins used to define the PDF, the minimum frequency of a bin within the PDF before that bin is considered for shallow clouds to form, and the critical cumulative frequency of bins required to trigger deep convection. A series of tests were undertaken to evaluate the sensitivity of the simulations to these parameters. Overall, the scheme was found to be relatively insensitive to each of the parameters.« less

Impact of WRF model PBL schemes on air quality simulations over Catalonia, Spain.

PubMed

Banks, R F; Baldasano, J M

2016-12-01

Here we analyze the impact of four planetary boundary-layer (PBL) parametrization schemes from the Weather Research and Forecasting (WRF) numerical weather prediction model on simulations of meteorological variables and predicted pollutant concentrations from an air quality forecast system (AQFS). The current setup of the Spanish operational AQFS, CALIOPE, is composed of the WRF-ARW V3.5.1 meteorological model tied to the Yonsei University (YSU) PBL scheme, HERMES v2 emissions model, CMAQ V5.0.2 chemical transport model, and dust outputs from BSC-DREAM8bv2. We test the performance of the YSU scheme against the Assymetric Convective Model Version 2 (ACM2), Mellor-Yamada-Janjic (MYJ), and Bougeault-Lacarrère (BouLac) schemes. The one-day diagnostic case study is selected to represent the most frequent synoptic condition in the northeast Iberian Peninsula during spring 2015; regional recirculations. It is shown that the ACM2 PBL scheme performs well with daytime PBL height, as validated against estimates retrieved using a micro-pulse lidar system (mean bias=-0.11km). In turn, the BouLac scheme showed WRF-simulated air and dew point temperature closer to METAR surface meteorological observations. Results are more ambiguous when simulated pollutant concentrations from CMAQ are validated against network urban, suburban, and rural background stations. The ACM2 scheme showed the lowest mean bias (-0.96μgm -3 ) with respect to surface ozone at urban stations, while the YSU scheme performed best with simulated nitrogen dioxide (-6.48μgm -3 ). The poorest results were with simulated particulate matter, with similar results found with all schemes tested. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

GPU-Accelerated Stony-Brook University 5-class Microphysics Scheme in WRF

NASA Astrophysics Data System (ADS)

Mielikainen, J.; Huang, B.; Huang, A.

2011-12-01

The Weather Research and Forecasting (WRF) model is a next-generation mesoscale numerical weather prediction system. Microphysics plays an important role in weather and climate prediction. Several bulk water microphysics schemes are available within the WRF, with different numbers of simulated hydrometeor classes and methods for estimating their size fall speeds, distributions and densities. Stony-Brook University scheme (SBU-YLIN) is a 5-class scheme with riming intensity predicted to account for mixed-phase processes. In the past few years, co-processing on Graphics Processing Units (GPUs) has been a disruptive technology in High Performance Computing (HPC). GPUs use the ever increasing transistor count for adding more processor cores. Therefore, GPUs are well suited for massively data parallel processing with high floating point arithmetic intensity. Thus, it is imperative to update legacy scientific applications to take advantage of this unprecedented increase in computing power. CUDA is an extension to the C programming language offering programming GPU's directly. It is designed so that its constructs allow for natural expression of data-level parallelism. A CUDA program is organized into two parts: a serial program running on the CPU and a CUDA kernel running on the GPU. The CUDA code consists of three computational phases: transmission of data into the global memory of the GPU, execution of the CUDA kernel, and transmission of results from the GPU into the memory of CPU. CUDA takes a bottom-up point of view of parallelism is which thread is an atomic unit of parallelism. Individual threads are part of groups called warps, within which every thread executes exactly the same sequence of instructions. To test SBU-YLIN, we used a CONtinental United States (CONUS) benchmark data set for 12 km resolution domain for October 24, 2001. A WRF domain is a geographic region of interest discretized into a 2-dimensional grid parallel to the ground. Each grid point has multiple levels, which correspond to various vertical heights in the atmosphere. The size of the CONUS 12 km domain is 433 x 308 horizontal grid points with 35 vertical levels. First, the entire SBU-YLIN Fortran code was rewritten in C in preparation of GPU accelerated version. After that, C code was verified against Fortran code for identical outputs. Default compiler options from WRF were used for gfortran and gcc compilers. The processing time for the original Fortran code is 12274 ms and 12893 ms for C version. The processing times for GPU implementation of SBU-YLIN microphysics scheme with I/O are 57.7 ms and 37.2 ms for 1 and 2 GPUs, respectively. The corresponding speedups are 213x and 330x compared to a Fortran implementation. Without I/O the speedup is 896x on 1 GPU. Obviously, ignoring I/O time speedup scales linearly with GPUs. Thus, 2 GPUs have a speedup of 1788x without I/O. Microphysics computation is just a small part of the whole WRF model. After having completely implemented WRF on GPU, the inputs for SBU-YLIN do not have to be transferred from CPU. Instead they are results of previous WRF modules. Therefore, the role of I/O is greatly diminished once all of WRF have been converted to run on GPUs. In the near future, we expect to have a WRF running completely on GPUs for a superior performance.

Application of Intel Many Integrated Core (MIC) architecture to the Yonsei University planetary boundary layer scheme in Weather Research and Forecasting model

NASA Astrophysics Data System (ADS)

Huang, Melin; Huang, Bormin; Huang, Allen H.

2014-10-01

The Weather Research and Forecasting (WRF) model provided operational services worldwide in many areas and has linked to our daily activity, in particular during severe weather events. The scheme of Yonsei University (YSU) is one of planetary boundary layer (PBL) models in WRF. The PBL is responsible for vertical sub-grid-scale fluxes due to eddy transports in the whole atmospheric column, determines the flux profiles within the well-mixed boundary layer and the stable layer, and thus provide atmospheric tendencies of temperature, moisture (including clouds), and horizontal momentum in the entire atmospheric column. The YSU scheme is very suitable for massively parallel computation as there are no interactions among horizontal grid points. To accelerate the computation process of the YSU scheme, we employ Intel Many Integrated Core (MIC) Architecture as it is a multiprocessor computer structure with merits of efficient parallelization and vectorization essentials. Our results show that the MIC-based optimization improved the performance of the first version of multi-threaded code on Xeon Phi 5110P by a factor of 2.4x. Furthermore, the same CPU-based optimizations improved the performance on Intel Xeon E5-2603 by a factor of 1.6x as compared to the first version of multi-threaded code.

Sensitivity of biogenic volatile organic compounds to land surface parameterizations and vegetation distributions in California

NASA Astrophysics Data System (ADS)

Zhao, Chun; Huang, Maoyi; Fast, Jerome D.; Berg, Larry K.; Qian, Yun; Guenther, Alex; Gu, Dasa; Shrivastava, Manish; Liu, Ying; Walters, Stacy; Pfister, Gabriele; Jin, Jiming; Shilling, John E.; Warneke, Carsten

2016-05-01

Current climate models still have large uncertainties in estimating biogenic trace gases, which can significantly affect atmospheric chemistry and secondary aerosol formation that ultimately influences air quality and aerosol radiative forcing. These uncertainties result from many factors, including uncertainties in land surface processes and specification of vegetation types, both of which can affect the simulated near-surface fluxes of biogenic volatile organic compounds (BVOCs). In this study, the latest version of Model of Emissions of Gases and Aerosols from Nature (MEGAN v2.1) is coupled within the land surface scheme CLM4 (Community Land Model version 4.0) in the Weather Research and Forecasting model with chemistry (WRF-Chem). In this implementation, MEGAN v2.1 shares a consistent vegetation map with CLM4 for estimating BVOC emissions. This is unlike MEGAN v2.0 in the public version of WRF-Chem that uses a stand-alone vegetation map that differs from what is used by land surface schemes. This improved modeling framework is used to investigate the impact of two land surface schemes, CLM4 and Noah, on BVOCs and examine the sensitivity of BVOCs to vegetation distributions in California. The measurements collected during the Carbonaceous Aerosol and Radiative Effects Study (CARES) and the California Nexus of Air Quality and Climate Experiment (CalNex) conducted in June of 2010 provided an opportunity to evaluate the simulated BVOCs. Sensitivity experiments show that land surface schemes do influence the simulated BVOCs, but the impact is much smaller than that of vegetation distributions. This study indicates that more effort is needed to obtain the most appropriate and accurate land cover data sets for climate and air quality models in terms of simulating BVOCs, oxidant chemistry and, consequently, secondary organic aerosol formation.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhao, Chun; Huang, Maoyi; Fast, Jerome D.

Current climate models still have large uncertainties in estimating biogenic trace gases, which can significantly affect atmospheric chemistry and secondary aerosol formation that ultimately influences air quality and aerosol radiative forcing. These uncertainties result from many factors, including uncertainties in land surface processes and specification of vegetation types, both of which can affect the simulated near-surface fluxes of biogenic volatile organic compounds (BVOCs). In this study, the latest version of Model of Emissions of Gases and Aerosols from Nature (MEGAN v2.1) is coupled within the land surface scheme CLM4 (Community Land Model version 4.0) in the Weather Research and Forecasting model withmore » chemistry (WRF-Chem). In this implementation, MEGAN v2.1 shares a consistent vegetation map with CLM4 for estimating BVOC emissions. This is unlike MEGAN v2.0 in the public version of WRF-Chem that uses a stand-alone vegetation map that differs from what is used by land surface schemes. This improved modeling framework is used to investigate the impact of two land surface schemes, CLM4 and Noah, on BVOCs and examine the sensitivity of BVOCs to vegetation distributions in California. The measurements collected during the Carbonaceous Aerosol and Radiative Effects Study (CARES) and the California Nexus of Air Quality and Climate Experiment (CalNex) conducted in June of 2010 provided an opportunity to evaluate the simulated BVOCs. Sensitivity experiments show that land surface schemes do influence the simulated BVOCs, but the impact is much smaller than that of vegetation distributions. This study indicates that more effort is needed to obtain the most appropriate and accurate land cover data sets for climate and air quality models in terms of simulating BVOCs, oxidant chemistry and, consequently, secondary organic aerosol formation.« less

Confronting the WRF and RAMS mesoscale models with innovative observations in the Netherlands: Evaluating the boundary layer heat budget

NASA Astrophysics Data System (ADS)

Steeneveld, G. J.; Tolk, L. F.; Moene, A. F.; Hartogensis, O. K.; Peters, W.; Holtslag, A. A. M.

2011-12-01

The Weather Research and Forecasting Model (WRF) and the Regional Atmospheric Mesoscale Model System (RAMS) are frequently used for (regional) weather, climate and air quality studies. This paper covers an evaluation of these models for a windy and calm episode against Cabauw tower observations (Netherlands), with a special focus on the representation of the physical processes in the atmospheric boundary layer (ABL). In addition, area averaged sensible heat flux observations by scintillometry are utilized which enables evaluation of grid scale model fluxes and flux observations at the same horizontal scale. Also, novel ABL height observations by ceilometry and of the near surface longwave radiation divergence are utilized. It appears that WRF in its basic set-up shows satisfactory model results for nearly all atmospheric near surface variables compared to field observations, while RAMS needed refining of its ABL scheme. An important inconsistency was found regarding the ABL daytime heat budget: Both model versions are only able to correctly forecast the ABL thermodynamic structure when the modeled surface sensible heat flux is much larger than both the eddy-covariance and scintillometer observations indicate. In order to clarify this discrepancy, model results for each term of the heat budget equation is evaluated against field observations. Sensitivity studies and evaluation of radiative tendencies and entrainment reveal that possible errors in these variables cannot explain the overestimation of the sensible heat flux within the current model infrastructure.

Improved meteorology from an updated WRF/CMAQ modeling ...

EPA Pesticide Factsheets

Realistic vegetation characteristics and phenology from the Moderate Resolution Imaging Spectroradiometer (MODIS) products improve the simulation for the meteorology and air quality modeling system WRF/CMAQ (Weather Research and Forecasting model and Community Multiscale Air Quality model) that employs the Pleim-Xiu land surface model (PX LSM). Recently, PX LSM WRF/CMAQ has been updated in vegetation, soil, and boundary layer processes resulting in improved 2 m temperature (T) and mixing ratio (Q), 10 m wind speed, and surface ozone simulations across the domain compared to the previous version for a period around August 2006. Yearlong meteorology simulations with the updated system demonstrate that MODIS input helps reduce bias of the 2 m Q estimation during the growing season from April to September. Improvements follow the green-up in the southeast from April and move toward the west and north through August. From October to March, MODIS input does not have much influence on the system because vegetation is not as active. The greatest effects of MODIS input include more accurate phenology, better representation of leaf area index (LAI) for various forest ecosystems and agricultural areas, and realistically sparse vegetation coverage in the western drylands. Despite the improved meteorology, MODIS input causes higher bias for the surface O3 simulation in April, August, and October in areas where MODIS LAI is much less than the base LAI. Thus, improvement

Range-Specific High-resolution Mesoscale Model Setup

NASA Technical Reports Server (NTRS)

Watson, Leela R.

2013-01-01

This report summarizes the findings from an AMU task to determine the best model configuration for operational use at the ER and WFF to best predict winds, precipitation, and temperature. The AMU ran test cases in the warm and cool seasons at the ER and for the spring and fall seasons at WFF. For both the ER and WFF, the ARW core outperformed the NMM core. Results for the ER indicate that the Lin microphysical scheme and the YSU PBL scheme is the optimal model configuration for the ER. It consistently produced the best surface and upper air forecasts, while performing fairly well for the precipitation forecasts. Both the Ferrier and Lin microphysical schemes in combination with the YSU PBL scheme performed well for WFF in the spring and fall seasons. The AMU has been tasked with a follow-on modeling effort to recommended local DA and numerical forecast model design optimized for both the ER and WFF to support space launch activities. The AMU will determine the best software and type of assimilation to use, as well as determine the best grid resolution for the initialization based on spatial and temporal availability of data and the wall clock run-time of the initialization. The AMU will transition from the WRF EMS to NU-WRF, a NASA-specific version of the WRF that takes advantage of unique NASA software and datasets. 37

Building the Fire Energetics and Emissions Research (FEER) Smoke Emissions Inventory Version 1.0

NASA Technical Reports Server (NTRS)

Ellison, Luke; Ichoku, Charles; Zhang, Feng; Wang, Jun

2014-01-01

The Fire Energetics and Emissions Research (FEER) group's new coefficient of emission global gridded product at 1x1 resolution that directly relates fire readiative energy (FRE) to smoke aerosol release, FEERv1.0 Ce, made its public debut in August 2013. Since then, steps have been taken to generate corresponding maps and totals of total particulate matter (PM) emissions using different sources of FRE, and subsequently to simulate the resulting PM(sub 2.5) in the WRF-Chem 3.5 model using emission rates from FEERv1.0 as well as other standard biomass burning emission inventories. An flowchart of the FEER algorithm to calculate Ce is outlined here along with a display of the resulting emissions of total PM globally and also regionally. The modeling results from the WRF-Chem3.5 simulations are also shown.

Regional Climate Model sesitivity to different parameterizations schemes with WRF over Spain

NASA Astrophysics Data System (ADS)

García-Valdecasas Ojeda, Matilde; Raquel Gámiz-Fortis, Sonia; Hidalgo-Muñoz, Jose Manuel; Argüeso, Daniel; Castro-Díez, Yolanda; Jesús Esteban-Parra, María

2015-04-01

The ability of the Weather Research and Forecasting (WRF) model to simulate the regional climate depends on the selection of an adequate combination of parameterization schemes. This study assesses WRF sensitivity to different parameterizations using six different runs that combined three cumulus, two microphysics and three surface/planetary boundary layer schemes in a topographically complex region such as Spain, for the period 1995-1996. Each of the simulations spanned a period of two years, and were carried out at a spatial resolution of 0.088° over a domain encompassing the Iberian Peninsula and nested in the coarser EURO-CORDEX domain (0.44° resolution). The experiments were driven by Interim ECMWF Re-Analysis (ERA-Interim) data. In addition, two different spectral nudging configurations were also analysed. The simulated precipitation and maximum and minimum temperatures from WRF were compared with Spain02 version 4 observational gridded datasets. The comparison was performed at different time scales with the purpose of evaluating the model capability to capture mean values and high-order statistics. ERA-Interim data was also compared with observations to determine the improvement obtained using dynamical downscaling with respect to the driving data. For this purpose, several parameters were analysed by directly comparing grid-points. On the other hand, the observational gridded data were grouped using a multistep regionalization to facilitate the comparison in term of monthly annual cycle and the percentiles of daily values analysed. The results confirm that no configuration performs best, but some combinations that produce better results could be chosen. Concerning temperatures, WRF provides an improvement over ERA-Interim. Overall, model outputs reduce the biases and the RMSE for monthly-mean maximum and minimum temperatures and are higher correlated with observations than ERA-Interim. The analysis shows that the Yonsei University planetary boundary layer scheme is the most appropriate parameterization in term of temperatures because it better describes monthly minimum temperatures and seems to perform well for maximum temperatures. Regarding precipitation, ERA-Interim time series are slightly higher correlated with observations than WRF, but the bias and the RMSE are largely worse. These results also suggest that CAM V.5.1 2-moment 5-class microphysics schemes should not be used due to the computational cost with no apparent gain with respect to simpler schemes such as WRF single-moment 3-class. For the convection scheme, this study suggests that Betts-Miller-Janjic scheme is an appropriate choice due to its robustness and Kain-Fritsch cumulus scheme should not be used over this region. KEY WORDS: Regional climate modelling, physics schemes, parameterizations, WRF. ACKNOWLEDGEMENTS This work has been financed by the projects P11-RNM-7941 (Junta de Andalucía-Spain) and CGL2013-48539-R (MINECO-Spain, FEDER).

A Goddard Multi-Scale Modeling System with Unified Physics

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo

2010-01-01

A multi-scale modeling system with unified physics has been developed at NASA Goddard Space Flight Center (GSFC). The system consists of an MMF, the coupled NASA Goddard finite-volume GCM (fvGCM) and Goddard Cumulus Ensemble model (GCE, a CRM); the state-of-the-art Weather Research and Forecasting model (WRF) and the stand alone GCE. These models can share the same microphysical schemes, radiation (including explicitly calculated cloud optical properties), and surface models that have been developed, improved and tested for different environments. In this talk, I will present: (1) A brief review on GCE model and its applications on the impact of the aerosol on deep precipitation processes, (2) The Goddard MMF and the major difference between two existing MMFs (CSU MMF and Goddard MMF), and preliminary results (the comparison with traditional GCMs), and (3) A discussion on the Goddard WRF version (its developments and applications). We are also performing the inline tracer calculation to comprehend the ph ysical processes (i.e., boundary layer and each quadrant in the boundary layer) related to the development and structure of hurricanes and mesoscale convective systems.

Distribution and transport of water vapor in the UTLS over the Tibetan Plateau as inferred from the MLS satellite data and WRF model simulations

NASA Astrophysics Data System (ADS)

Jain, S.; Kar, S. C.

2016-12-01

Water vapor is an important minor constituent in the lower stratosphere as it influences the stratospheric chemistry and total radiation budget. The spatial distribution of water vapor mixing ratio (WVMR) obtained from Aura Microwave Limb Sounder (MLS) satellite at 100 hPa level shows prominent maxima over the Tibetan Plateau during August 2015. The Asian monsoon upper level anticyclone is also known to occur over this region during this period. The Indian Meteorological Department (IMD) and National Centre of Medium Range Weather Forecasting (NCMRWF) observed daily gridded rainfall data shows moderate to heavy rainfall over the Tibetan Plateau, suggesting active convection from 26 July to 10 August 2015. The atmospheric conditions are simulated over the Asian region for the 15-day period using the Weather Research Forecasting (WRF) model. The simulations are carried out using two nested domains with resolution of 12 km and 4 km. The initial and boundary conditions are taken from the NGFS (up-graded version of the NCEP GFS) data. The WRF WVMR profiles are observed to be comparatively moist than the MLS profiles in the UTLS region over the Tibetan Plateau. This may be due to the relatively higher temperatures (1-2 K) simulated in the WRF model near 100 hPa level. It is noted that the WRF model has a drying tendency at all the levels. The UTLS WVMR and temperatures show poor sensitivity to the convective schemes. The parent domain and the explicit convective scheme simulate almost same moisture over time in the inner domain. The cloud micro-physics is observed to play a rather important role in controlling the UTLS water vapor content. The WSM-6 convective scheme is observed to simulate the UTLS moisture comparatively well and therefore the processes associated with the formation of ice, snow and graupel formation may be of much more importance in controlling the UTLS WVMR in the WRF model. The 24 hr, 48 hr and 72 hr forecast averaged for the 15-day period shows that over the Tibetan Plateau, high WVMR in the UTLS is not centered within the anticyclone, contrary to what has been shown by earlier studies. Similar simulations are also being carried out using the Era-interim initial and boundary conditions to confirm the above findings.

Real-Time Kennedy Space Center and Cape Canaveral Air Force Station High-Resolution Model Implementation and Verification

NASA Technical Reports Server (NTRS)

Shafer, Jaclyn A.; Watson, Leela R.

2015-01-01

Customer: NASA's Launch Services Program (LSP), Ground Systems Development and Operations (GSDO), and Space Launch System (SLS) programs. NASA's LSP, GSDO, SLS and other programs at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) use the daily and weekly weather forecasts issued by the 45th Weather Squadron (45 WS) as decision tools for their day-to-day and launch operations on the Eastern Range (ER). For example, to determine if they need to limit activities such as vehicle transport to the launch pad, protect people, structures or exposed launch vehicles given a threat of severe weather, or reschedule other critical operations. The 45 WS uses numerical weather prediction models as a guide for these weather forecasts, particularly the Air Force Weather Agency (AFWA) 1.67 kilometer Weather Research and Forecasting (WRF) model. Considering the 45 WS forecasters' and Launch Weather Officers' (LWO) extensive use of the AFWA model, the 45 WS proposed a task at the September 2013 Applied Meteorology Unit (AMU) Tasking Meeting requesting the AMU verify this model. Due to the lack of archived model data available from AFWA, verification is not yet possible. Instead, the AMU proposed to implement and verify the performance of an ER version of the AMU high-resolution WRF Environmental Modeling System (EMS) model (Watson 2013) in real-time. The tasking group agreed to this proposal; therefore the AMU implemented the WRF-EMS model on the second of two NASA AMU modeling clusters. The model was set up with a triple-nested grid configuration over KSC/CCAFS based on previous AMU work (Watson 2013). The outer domain (D01) has 12-kilometer grid spacing, the middle domain (D02) has 4-kilometer grid spacing, and the inner domain (D03) has 1.33-kilometer grid spacing. The model runs a 12-hour forecast every hour, D01 and D02 domain outputs are available once an hour and D03 is every 15 minutes during the forecast period. The AMU assessed the WRF-EMS 1.33-kilometer domain model performance for the 2014 warm season (May-September). Verification statistics were computed using the Model Evaluation Tools, which compared the model forecasts to observations. The mean error values were close to 0 and the root mean square error values were less than 1.8 for mean sea-level pressure (millibars), temperature (degrees Kelvin), dewpoint temperature (degrees Kelvin), and wind speed (per millisecond), all very small differences between the forecast and observations considering the normal magnitudes of the parameters. The precipitation forecast verification results showed consistent under-forecasting of the precipitation object size. This could be an artifact of calculating the statistics for each hour rather than for the entire 12-hour period. The AMU will continue to generate verification statistics for the 1.33-kilometer WRF-EMS domain as data become available in future cool and warm seasons. More data will produce more robust statistics and reveal a more accurate assessment of model performance. Once the formal task was complete, the AMU conducted additional work to better understand the wind direction results. The results were stratified diurnally and by wind speed to determine what effects the stratifications would have on the model wind direction verification statistics. The results are summarized in the addendum at the end of this report. In addition to verifying the model's performance, the AMU also made the output available in the Advanced Weather Interactive Processing System II (AWIPS II). This allows the 45 WS and AMU staff to customize the model output display on the AMU and Range Weather Operations AWIPS II client computers and conduct real-time subjective analyses. In the future, the AMU will implement an updated version of the WRF-EMS model that incorporates local data assimilation. This model will also run in real-time and be made available in AWIPS II.

Modeling urban air pollution in Budapest using WRF-Chem model

NASA Astrophysics Data System (ADS)

Kovács, Attila; Leelőssy, Ádám; Lagzi, István; Mészáros, Róbert

2017-04-01

Air pollution is a major problem for urban areas since the industrial revolution, including Budapest, the capital and largest city of Hungary. The main anthropogenic sources of air pollutants are industry, traffic and residential heating. In this study, we investigated the contribution of major industrial point sources to the urban air pollution in Budapest. We used the WRF (Weather Research and Forecasting) nonhydrostatic mesoscale numerical weather prediction system online coupled with chemistry (WRF-Chem, version 3.6).The model was configured with three nested domains with grid spacings of 15, 5 and 1 km, representing Central Europe, the Carpathian Basin and Budapest with its surrounding area. Emission data was obtained from the National Environmental Information System. The point source emissions were summed in their respective cells in the second nested domain according to latitude-longitude coordinates. The main examined air pollutants were carbon monoxide (CO) and nitrogen oxides (NOx), from which the secondary compound, ozone (O3) forms through chemical reactions. Simulations were performed under different weather conditions and compared to observations from the automatic monitoring site of the Hungarian Air Quality Network. Our results show that the industrial emissions have a relatively weak role in the urban background air pollution, confirming the effect of industrial developments and regulations in the recent decades. However, a few significant industrial sources and their impact area has been demonstrated.

Can green roofs reduce urban heat stress in vulnerable urban communities: A coupled atmospheric and social modeling approach

NASA Astrophysics Data System (ADS)

Sharma, A.; Woodruff, S.; Budhathoki, M.; Hamlet, A. F.; Fernando, H. J. S.; Chen, F.

2017-12-01

Urban areas provide organized, engineered, sociological and economical infrastructure designed to provide a high quality of life, but the implementation and management of urban infrastructure has been a continued challenge. Increasing urbanization, warming climate, as well as anthropogenic heat emissions that accompany urban development generates "stress". This rapidly increasing `urban stress' affects the sustainability of cities, making populations more vulnerable to extreme hazards, such as heat. Cities are beginning to extensively use green roofs as a potential urban heat mitigation strategy. This study explores the potential of green roofs to reduce summertime temperatures in the most vulnerable neighborhoods of the Chicago metropolitan area by combining social vulnerability indices (a function of exposure, sensitivity and adaptive capacity), and temperatures from mesoscale model. Numerical simulations using urbanized version the Advanced Research Weather Research and Forecasting (WRF) model were performed to measure rooftop temperatures, a representative variable for exposure in this study. The WRF simulations were dynamically coupled with a green roof algorithm as a part of urban parameterization within WRF. Specifically, the study examines roof surface temperature with changing green roof fractions and how would they help reduce exposure to heat stress for vulnerable urban communities. This study shows an example of applied research that can directly benefit urban communities and be used by urban planners to evaluate mitigation strategies.

Improvements to the WRF-Chem 3.5.1 model for quasi-hemispheric simulations of aerosols and ozone in the Arctic

DOE PAGES

Marelle, Louis; Raut, Jean-Christophe; Law, Kathy S.; ...

2017-01-01

In this study, the WRF-Chem regional model is updated to improve simulated short-lived pollutants (e.g., aerosols, ozone) in the Arctic. Specifically, we include in WRF-Chem 3.5.1 (with SAPRC-99 gas-phase chemistry and MOSAIC aerosols) (1) a correction to the sedimentation of aerosols, (2) dimethyl sulfide (DMS) oceanic emissions and gas-phase chemistry, (3) an improved representation of the dry deposition of trace gases over seasonal snow, and (4) an UV-albedo dependence on snow and ice cover for photolysis calculations. We also (5) correct the representation of surface temperatures over melting ice in the Noah Land Surface Model and (6) couple and further test the recent KF-CuP (Kain–Fritsch +more » Cumulus Potential) cumulus parameterization that includes the effect of cumulus clouds on aerosols and trace gases. The updated model is used to perform quasi-hemispheric simulations of aerosols and ozone, which are evaluated against surface measurements of black carbon (BC), sulfate, and ozone as well as airborne measurements of BC in the Arctic. The updated model shows significant improvements in terms of seasonal aerosol cycles at the surface and root mean square errors (RMSEs) for surface ozone, aerosols, and BC aloft, compared to the base version of the model and to previous large-scale evaluations of WRF-Chem in the Arctic. These improvements are mostly due to the inclusion of cumulus effects on aerosols and trace gases in KF-CuP (improved RMSE for surface BC and BC profiles, surface sulfate, and surface ozone), the improved surface temperatures over sea ice (surface ozone, BC, and sulfate), and the updated trace gas deposition and UV albedo over snow and ice (improved RMSE and correlation for surface ozone). DMS emissions and chemistry improve surface sulfate at all Arctic sites except Zeppelin, and correcting aerosol sedimentation has little influence on aerosols except in the upper troposphere.« less

Improvements to the WRF-Chem 3.5.1 model for quasi-hemispheric simulations of aerosols and ozone in the Arctic

DOE Office of Scientific and Technical Information (OSTI.GOV)

Marelle, Louis; Raut, Jean-Christophe; Law, Kathy S.

In this study, the WRF-Chem regional model is updated to improve simulated short-lived pollutants (e.g., aerosols, ozone) in the Arctic. Specifically, we include in WRF-Chem 3.5.1 (with SAPRC-99 gas-phase chemistry and MOSAIC aerosols) (1) a correction to the sedimentation of aerosols, (2) dimethyl sulfide (DMS) oceanic emissions and gas-phase chemistry, (3) an improved representation of the dry deposition of trace gases over seasonal snow, and (4) an UV-albedo dependence on snow and ice cover for photolysis calculations. We also (5) correct the representation of surface temperatures over melting ice in the Noah Land Surface Model and (6) couple and further test the recent KF-CuP (Kain–Fritsch +more » Cumulus Potential) cumulus parameterization that includes the effect of cumulus clouds on aerosols and trace gases. The updated model is used to perform quasi-hemispheric simulations of aerosols and ozone, which are evaluated against surface measurements of black carbon (BC), sulfate, and ozone as well as airborne measurements of BC in the Arctic. The updated model shows significant improvements in terms of seasonal aerosol cycles at the surface and root mean square errors (RMSEs) for surface ozone, aerosols, and BC aloft, compared to the base version of the model and to previous large-scale evaluations of WRF-Chem in the Arctic. These improvements are mostly due to the inclusion of cumulus effects on aerosols and trace gases in KF-CuP (improved RMSE for surface BC and BC profiles, surface sulfate, and surface ozone), the improved surface temperatures over sea ice (surface ozone, BC, and sulfate), and the updated trace gas deposition and UV albedo over snow and ice (improved RMSE and correlation for surface ozone). DMS emissions and chemistry improve surface sulfate at all Arctic sites except Zeppelin, and correcting aerosol sedimentation has little influence on aerosols except in the upper troposphere.« less

Improvements to the WRF-Chem 3.5.1 model for quasi-hemispheric simulations of aerosols and ozone in the Arctic

NASA Astrophysics Data System (ADS)

Marelle, Louis; Raut, Jean-Christophe; Law, Kathy S.; Berg, Larry K.; Fast, Jerome D.; Easter, Richard C.; Shrivastava, Manish; Thomas, Jennie L.

2017-10-01

In this study, the WRF-Chem regional model is updated to improve simulated short-lived pollutants (e.g., aerosols, ozone) in the Arctic. Specifically, we include in WRF-Chem 3.5.1 (with SAPRC-99 gas-phase chemistry and MOSAIC aerosols) (1) a correction to the sedimentation of aerosols, (2) dimethyl sulfide (DMS) oceanic emissions and gas-phase chemistry, (3) an improved representation of the dry deposition of trace gases over seasonal snow, and (4) an UV-albedo dependence on snow and ice cover for photolysis calculations. We also (5) correct the representation of surface temperatures over melting ice in the Noah Land Surface Model and (6) couple and further test the recent KF-CuP (Kain-Fritsch + Cumulus Potential) cumulus parameterization that includes the effect of cumulus clouds on aerosols and trace gases. The updated model is used to perform quasi-hemispheric simulations of aerosols and ozone, which are evaluated against surface measurements of black carbon (BC), sulfate, and ozone as well as airborne measurements of BC in the Arctic. The updated model shows significant improvements in terms of seasonal aerosol cycles at the surface and root mean square errors (RMSEs) for surface ozone, aerosols, and BC aloft, compared to the base version of the model and to previous large-scale evaluations of WRF-Chem in the Arctic. These improvements are mostly due to the inclusion of cumulus effects on aerosols and trace gases in KF-CuP (improved RMSE for surface BC and BC profiles, surface sulfate, and surface ozone), the improved surface temperatures over sea ice (surface ozone, BC, and sulfate), and the updated trace gas deposition and UV albedo over snow and ice (improved RMSE and correlation for surface ozone). DMS emissions and chemistry improve surface sulfate at all Arctic sites except Zeppelin, and correcting aerosol sedimentation has little influence on aerosols except in the upper troposphere.

A comparison of daily precipitation metrics downscaled using SDSM and WRF + WRFDA models over the Iberian Peninsula.

NASA Astrophysics Data System (ADS)

José González-Rojí, Santos; Wilby, Robert L.; Sáenz, Jon; Ibarra-Berastegi, Gabriel

2017-04-01

Downscaling via the Statistical DownScaling Model (SDSM) version 5.2 and two different configurations of the dynamical WRF model (with and without 3DVAR data assimilation) was evaluated for the estimation of daily precipitation over 21 sites across the Iberian Peninsula during the period 2010-2014. Six different strategies were used to calibrate the SDSM model. These options cover (1) use of NCEP/NCAR R1 Reanalysis and (2) ERA Interim data for downscaling predictor variables calibrated with data from periods (3) 1948-2009 (NCEP/NCAR R1) and (4) 1979-2009 (NCEP/NCAR R1 and ERA Interim). Additionally, for the ERA Interim case, two different grid resolutions have been used, (5) 2.5° and (6) 0.75°. On the other side, for the NCEP/NCAR R1 case, only the 2.5° resolution has been used. Configuring the SDSM model in this way allows testing the sensitivity of the results to different origins of the predictors, fit to different calibration periods and use of different reanalysis resolutions. On the other hand, ERA Interim data at the highest resolution was used as the initial/boundary conditions to run WRF simulations with a 15 km x 15 km horizontal resolution over the Iberian Peninsula, for two different configurations. The first experiment (N) was run using the same configuration typically used for numerical downscaling, with information being fed through the boundaries of the domain. The second experiment (D) was run using 3DVAR data assimilation at 00UTC, 06UTC, 12UTC and 18UTC. In both cases, WRF simulations were run over the period 2009-2014, using the first year (2009) as spin-up for the soil model. Results from the WRF N and D runs and comparable SDSM set up for the period 2010-2014 were evaluated using observations from ECA and E-OBS datasets. In each case, model skill was assessed using seven daily precipitation metrics (absolute mean, wet-day intensity, 90th percentile, maximum 5-day total, maximum number of consecutive dry days, fraction of total from heavy events and number of heavy events defined here as values over the threshold of 90th percentile. Our results show that the SDSM model improves its behaviour when using predictors from the ERA Interim Reanalysis. Improvements are even more impressive when using the 0.75° resolution for ERA Interim. Better results than using WRF D are obtained with this configuration of the SDSM model for mean precipitation and precipitation intensity. Overall, the analysis reveals the extent to which the skill of SDSM can be improved through judicious choice of downscaling predictor source, grid resolution and calibration period. Moreover, the computationally efficient SDSM tool can achieve comparable skill to WRF over a range of precipitation metrics and the contrasting rainfall regimes of the Iberian Peninsula.

Automated turbulence forecasts for aviation hazards

NASA Astrophysics Data System (ADS)

Sharman, R.; Frehlich, R.; Vandenberghe, F.

2010-09-01

An operational turbulence forecast system for commercial and aviation use is described that is based on an ensemble of turbulence diagnostics derived from standard NWP model outputs. In the U. S. this forecast product is named GTG (Graphical Turbulence Guidance) and has been described in detail in Sharman et al., WAF 2006. Since turbulence has many sources in the atmosphere, the ensemble approach of combining diagnostics has been shown to provide greater statistical accuracy than the use of a single diagnostic, or of a subgrid tke parameterization. GTG is sponsored by the FAA, and has undergone rigorous accuracy, safety, and usability evaluations. The GTG product is now hosted on NOAA's Aviation Data Service (ADDS), web site (http://aviationweather.gov/), for access by pilots, air traffic controllers, and dispatchers. During this talk the various turbulence diagnostics, their statistical properties, and their relative performance (based on comparisons to observations) will be presented. Importantly, the model output is ɛ1/3 (where ɛ is the eddy dissipation rate), so is aircraft independent. The diagnostics are individually and collectively calibrated so that their PDFs satisfy the expected log normal distribution of ɛ^1/3. Some of the diagnostics try to take into account the role of gravity waves and inertia-gravity waves in the turbulence generation process. Although the current GTG product is based on the RUC forecast model running over the CONUS, it is transitioning to a WRF based product, and in fact WRF-based versions are currently running operationally over Taiwan and has also been implemented for use by the French Navy in climatological studies. Yet another version has been developed which uses GFS model output to provide global turbulence forecasts. Thus the forecast product is available as a postprocessing program for WRF or other model output and provides 3D maps of turbulence likelihood of any region where NWP model data is available. Although the current GTG has been used mainly for large commercial aircraft, since the output is aircraft independent it could readily be scaled to smaller aircraft such as UAVs. Further, the ensemble technique allows the diagnostics to be used to form probabilistic forecasts, in a manner similar to ensemble NWP forecasts.

High-Resolution NU-WRF Simulations of a Deep Convective-Precipitation System During MC3E. Part 1; Comparisons Between Goddard Microphysics Schemes and Observations

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Wu, Di; Lang, Stephen; Chern, Jiundar; Peters-Lidard, Christa; Fridlind, Ann; Matsui, Toshihisa

2015-01-01

The Goddard microphysics scheme was recently improved by adding a 4th ice class (frozen dropshail). This new 4ICE scheme was implemented and tested in the Goddard Cumulus Ensemble model (GCE) for an intense continental squall line and a moderate,less-organized continental case. Simulated peak radar reflectivity profiles were improved both in intensity and shape for both cases as were the overall reflectivity probability distributions versus observations. In this study, the new Goddard 4ICE scheme is implemented into the regional-scale NASA Unified - Weather Research and Forecasting model (NU-WRF) and tested on an intense mesoscale convective system that occurred during the Midlatitude Continental Convective Clouds Experiment (MC3E). The NU42WRF simulated radar reflectivities, rainfall intensities, and vertical and horizontal structure using the new 4ICE scheme agree as well as or significantly better with observations than when using previous versions of the Goddard 3ICE (graupel or hail) schemes. In the 4ICE scheme, the bin microphysics-based rain evaporation correction produces more erect convective cores, while modification of the unrealistic collection of ice by dry hail produces narrow and intense cores, allowing more slow-falling snow to be transported rearward. Together with a revised snow size mapping, the 4ICE scheme produces a more horizontally stratified trailing stratiform region with a broad, more coherent light rain area. In addition, the NU-WRF 4ICE simulated radar reflectivity distributions are consistent with and generally superior to those using the GCE due to the less restrictive open lateral boundaries

Data Assimilation and Regional Forecasts Using Atmospheric InfraRed Sounder (AIRS) Profiles

NASA Technical Reports Server (NTRS)

Chou, Shih-Hung; Zavodsky, Bradley; Jedlovec, Gary

2009-01-01

In data sparse regions, remotely-sensed observations can be used to improve analyses, which in turn should lead to better forecasts. One such source comes from the Atmospheric Infrared Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), provides temperature and moisture profiles with an accuracy comparable to that of radiosondes. The purpose of this paper is to describe a procedure to optimally assimilate AIRS thermodynamic profiles--obtained from the version 5.0 Earth Observing System (EOS) science team retrieval algorithm-into a regional configuration of the Weather Research and Forecasting (WRF) model using WRF-Var. The paper focuses on development of background error covariances for the regional domain and background field type, a methodology for ingesting AIRS profiles as separate over-land and over-water retrievals with different error characteristics, and utilization of level-by-level quality indicators to select only the highest quality data. The assessment of the impact of the AIRS profiles on WRF-Var analyses will focus on intelligent use of the quality indicators, optimized tuning of the WRF-Var, and comparison of analysis soundings to radiosondes. The analyses will be used to conduct a month-long series of regional forecasts over the continental U.S. The long-tern1 impact of AIRS profiles on forecast will be assessed against verifying radiosonde and stage IV precipitation data.

Data Assimilation and Regional Forecasts using Atmospheric InfraRed Sounder (AIRS) Profiles

NASA Technical Reports Server (NTRS)

Zabodsky, Brad; Chou, Shih-Hung; Jedlovec, Gary J.

2009-01-01

In data sparse regions, remotely-sensed observations can be used to improve analyses, which in turn should lead to better forecasts. One such source comes from the Atmospheric Infrared Sounder (AIRS), which, together with the Advanced Microwave Sounding Unit (AMSU), provides temperature and moisture profiles with an accuracy comparable to that of radionsondes. The purpose of this poster is to describe a procedure to optimally assimilate AIRS thermodynamic profiles, obtained from the version 5.0 Earth Observing System (EOS) science team retrieval algorithm, into a regional configuration of the Weather Research and Forecasting (WRF) model using WRF-Var. The poster focuses on development of background error covariances for the regional domain and background field type, a methodology for ingesting AIRS profiles as separate over-land and over-water retrievals with different error characteristics, and utilization of level-by-level quality indicators to select only the highest quality data. The assessment of the impact of the AIRS profiles on WRF-Var analyses will focus on intelligent use of the quality indicators, optimized tuning of the WRF-Var, and comparison of analysis soundings to radiosondes. The analyses are used to conduct a month-long series of regional forecasts over the continental U.S. The long-term impact of AIRS profiles on forecast will be assessed against NAM analyses and stage IV precipitation data.

A framework for WRF to WRF-IBM grid nesting to enable multiscale simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wiersema, David John; Lundquist, Katherine A.; Chow, Fotini Katapodes

With advances in computational power, mesoscale models, such as the Weather Research and Forecasting (WRF) model, are often pushed to higher resolutions. As the model’s horizontal resolution is refined, the maximum resolved terrain slope will increase. Because WRF uses a terrain-following coordinate, this increase in resolved terrain slopes introduces additional grid skewness. At high resolutions and over complex terrain, this grid skewness can introduce large numerical errors that require methods, such as the immersed boundary method, to keep the model accurate and stable. Our implementation of the immersed boundary method in the WRF model, WRF-IBM, has proven effective at microscalemore » simulations over complex terrain. WRF-IBM uses a non-conforming grid that extends beneath the model’s terrain. Boundary conditions at the immersed boundary, the terrain, are enforced by introducing a body force term to the governing equations at points directly beneath the immersed boundary. Nesting between a WRF parent grid and a WRF-IBM child grid requires a new framework for initialization and forcing of the child WRF-IBM grid. This framework will enable concurrent multi-scale simulations within the WRF model, improving the accuracy of high-resolution simulations and enabling simulations across a wide range of scales.« less

A Goddard Multi-Scale Modeling System with Unified Physics

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo

2010-01-01

A multi-scale modeling system with unified physics has been developed at NASA Goddard Space Flight Center (GSFC). The system consists of an MMF, the coupled NASA Goddard finite-volume GCM (fvGCM) and Goddard Cumulus Ensemble model (GCE, a CRM); the state-of-the-art Weather Research and Forecasting model (WRF) and the stand alone GCE. These models can share the same microphysical schemes, radiation (including explicitly calculated cloud optical properties), and surface models that have been developed, improved and tested for different environments. In this talk, I will present: (1) A brief review on GCE model and its applications on the impact of the aerosol on deep precipitation processes, (2) The Goddard MMF and the major difference between two existing MMFs (CSU MMF and Goddard MMF), and preliminary results (the comparison with traditional GCMs), and (3) A discussion on the Goddard WRF version (its developments and applications). We are also performing the inline tracer calculation to comprehend the physical processes (i.e., boundary layer and each quadrant in the boundary layer) related to the development and structure of hurricanes and mesoscale convective systems. In addition, high - resolution (spatial. 2km, and temporal, I minute) visualization showing the model results will be presented.

Bias Correction for Assimilation of Retrieved AIRS Profiles of Temperature and Humidity

NASA Technical Reports Server (NTRS)

Blakenship, Clay; Zavodsky, Bradley; Blackwell, William

2014-01-01

The Atmospheric Infrared Sounder (AIRS) is a hyperspectral radiometer aboard NASA's Aqua satellite designed to measure atmospheric profiles of temperature and humidity. AIRS retrievals are assimilated into the Weather Research and Forecasting (WRF) model over the North Pacific for some cases involving "atmospheric rivers". These events bring a large flux of water vapor to the west coast of North America and often lead to extreme precipitation in the coastal mountain ranges. An advantage of assimilating retrievals rather than radiances is that information in partly cloudy fields of view can be used. Two different Level 2 AIRS retrieval products are compared: the Version 6 AIRS Science Team standard retrievals and a neural net retrieval from MIT. Before assimilation, a bias correction is applied to adjust each layer of retrieved temperature and humidity so the layer mean values agree with a short-term model climatology. WRF runs assimilating each of the products are compared against each other and against a control run with no assimilation. Forecasts are against ERA reanalyses.

First Assessment of Itaipu Dam Ensemble Inflow Forecasting System

NASA Astrophysics Data System (ADS)

Mainardi Fan, Fernando; Machado Vieira Lisboa, Auder; Gomes Villa Trinidad, Giovanni; Rógenes Monteiro Pontes, Paulo; Collischonn, Walter; Tucci, Carlos; Costa Buarque, Diogo

2017-04-01

Inflow forecasting for Hydropower Plants (HPP) Dams is one of the prominent uses for hydrological forecasts. A very important HPP in terms of energy generation for South America is the Itaipu Dam, located in the Paraná River, between Brazil and Paraguay countries, with a drainage area of 820.000km2. In this work, we present the development of an ensemble forecasting system for Itaipu, operational since November 2015. The system is based in the MGB-IPH hydrological model, includes hydrodynamics simulations of the main river, and is run every day morning forced by seven different rainfall forecasts: (i) CPTEC-ETA 15km; (ii) CPTEC-BRAMS 5km; (iii) SIMEPAR WRF Ferrier; (iv) SIMEPAR WRF Lin; (v) SIMEPAR WRF Morrison; (vi) SIMEPAR WRF WDM6; (vii) SIMEPAR MEDIAN. The last one (vii) corresponds to the median value of SIMEPAR WRF model versions (iii to vi) rainfall forecasts. Besides the developed system, the "traditional" method for inflow forecasting generation for the Itaipu Dam is also run every day. This traditional method consists in the approximation of the future inflow based on the discharge tendency of upstream telemetric gauges. Nowadays, after all the forecasts are run, the hydrology team of Itaipu develop a consensus forecast, based on all obtained results, which is the one used for the Itaipu HPP Dam operation. After one year of operation a first evaluation of the Ensemble Forecasting System was conducted. Results show that the system performs satisfactory for rising flows up to five days lead time. However, some false alarms were also issued by most ensemble members in some cases. And not in all cases the system performed better than the traditional method, especially during hydrograph recessions. In terms of meteorological forecasts, some members usage are being discontinued. In terms of the hydrodynamics representation, it seems that a better information of rivers cross section could improve hydrographs recession curves forecasts. Those opportunities for improvements are currently being addressed in the system next update.

Simulation of a severe convective storm using a numerical model with explicitly incorporated aerosols

NASA Astrophysics Data System (ADS)

Lompar, Miloš; Ćurić, Mladjen; Romanic, Djordje

2017-09-01

Despite an important role the aerosols play in all stages of cloud lifecycle, their representation in numerical weather prediction models is often rather crude. This paper investigates the effects the explicit versus implicit inclusion of aerosols in a microphysics parameterization scheme in Weather Research and Forecasting (WRF) - Advanced Research WRF (WRF-ARW) model has on cloud dynamics and microphysics. The testbed selected for this study is a severe mesoscale convective system with supercells that struck west and central parts of Serbia in the afternoon of July 21, 2014. Numerical products of two model runs, i.e. one with aerosols explicitly (WRF-AE) included and another with aerosols implicitly (WRF-AI) assumed, are compared against precipitation measurements from surface network of rain gauges, as well as against radar and satellite observations. The WRF-AE model accurately captured the transportation of dust from the north Africa over the Mediterranean and to the Balkan region. On smaller scales, both models displaced the locations of clouds situated above west and central Serbia towards southeast and under-predicted the maximum values of composite radar reflectivity. Similar to satellite images, WRF-AE shows the mesoscale convective system as a merged cluster of cumulonimbus clouds. Both models over-predicted the precipitation amounts; WRF-AE over-predictions are particularly pronounced in the zones of light rain, while WRF-AI gave larger outliers. Unlike WRF-AI, the WRF-AE approach enables the modelling of time evolution and influx of aerosols into the cloud which could be of practical importance in weather forecasting and weather modification. Several likely causes for discrepancies between models and observations are discussed and prospects for further research in this field are outlined.

Dynamical Downscaling of Meteorology from a Global Model by WRF towards Resolving US PM2.5 Distributions for the Mid 21st Century

NASA Astrophysics Data System (ADS)

Kunwar, S.; Bowden, J.; Milly, G.; Previdi, M. J.; Fiore, A. M.; West, J. J.

2017-12-01

In the coming decades, anthropogenically induced climate change will likely impact PM2.5 through both changing meteorology and feedback in natural emissions. A major goal of our project is to assess changes in PM2.5 levels over the continental US due to climate variability and change for the period 2005-2065. We will achieve this by using regional models to dynamically downscale coarse resolution (20 × 20) meteorology and air chemistry from a global model to finer spatial resolution (12 km), improving air quality projections for regions and subregions of the US (NE, SE, SW, NW, Midwest, Intermountain West). We downscale from GFDL CM3 simulations of the RCP8.5 scenario for the years 2006-2100 with aerosol and ozone precursor emissions fixed at 2005 levels. We carefully select model years from the global simulations that sample the range of PM2.5 distributions for different US regions at mid 21st century (2050-2065). Here we will show results for the meteorological downscaling (using WRF version 3.8.1) for this project, including a performance evaluation for meteorological variables with respect to the global model. In the future, the downscaled meteorology presented here will be used to drive air quality downscaling in CMAQ (version 5.2). Analysis of the resulting PM2.5 statistics for US regions, as well as the drivers for PM2.5 changes, will be important in supporting informed policies for air quality (also health and visibility) planning for different US regions for the next five decades.

Dynamical Downscaling of NASA/GISS ModelE: Continuous, Multi-Year WRF Simulations

NASA Astrophysics Data System (ADS)

Otte, T.; Bowden, J. H.; Nolte, C. G.; Otte, M. J.; Herwehe, J. A.; Faluvegi, G.; Shindell, D. T.

2010-12-01

The WRF Model is being used at the U.S. EPA for dynamical downscaling of the NASA/GISS ModelE fields to assess regional impacts of climate change in the United States. The WRF model has been successfully linked to the ModelE fields in their raw hybrid vertical coordinate, and continuous, multi-year WRF downscaling simulations have been performed. WRF will be used to downscale decadal time slices of ModelE for recent past, current, and future climate as the simulations being conducted for the IPCC Fifth Assessment Report become available. This presentation will focus on the sensitivity to interior nudging within the RCM. The use of interior nudging for downscaled regional climate simulations has been somewhat controversial over the past several years but has been recently attracting attention. Several recent studies that have used reanalysis (i.e., verifiable) fields as a proxy for GCM input have shown that interior nudging can be beneficial toward achieving the desired downscaled fields. In this study, the value of nudging will be shown using fields from ModelE that are downscaled using WRF. Several different methods of nudging are explored, and it will be shown that the method of nudging and the choices made with respect to how nudging is used in WRF are critical to balance the constraint of ModelE against the freedom of WRF to develop its own fields.

Application of WRF/Chem over East Asia: Part I. Model evaluation and intercomparison with MM5/CMAQ

NASA Astrophysics Data System (ADS)

Zhang, Yang; Zhang, Xin; Wang, Litao; Zhang, Qiang; Duan, Fengkui; He, Kebin

2016-01-01

In this work, the application of the online-coupled Weather Research and Forecasting model with chemistry (WRF/Chem) version 3.3.1 is evaluated over East Asia for January, April, July, and October 2005 and compared with results from a previous application of an offline model system, i.e., the Mesoscale Model and Community Multiple Air Quality modeling system (MM5/CMAQ). The evaluation of WRF/Chem is performed using multiple observational datasets from satellites and surface networks in mainland China, Hong Kong, Taiwan, and Japan. WRF/Chem simulates well specific humidity (Q2) and downward longwave and shortwave radiation (GLW and GSW) with normalized mean biases (NMBs) within 24%, but shows moderate to large biases for temperature at 2-m (T2) (NMBs of -9.8% to 75.6%) and precipitation (NMBs of 11.4-92.7%) for some months, and wind speed at 10-m (WS10) (NMBs of 66.5-101%), for all months, indicating some limitations in the YSU planetary boundary layer scheme, the Purdue Lin cloud microphysics, and the Grell-Devenyi ensemble scheme. WRF/Chem can simulate the column abundances of gases reasonably well with NMBs within 30% for most months but moderately to significantly underpredicts the surface concentrations of major species at all sites in nearly all months with NMBs of -72% to -53.8% for CO, -99.4% to -61.7% for NOx, -84.2% to -44.5% for SO2, -63.9% to -25.2% for PM2.5, and -68.9% to 33.3% for PM10, and aerosol optical depth in all months except for October with NMBs of -38.7% to -16.2%. The model significantly overpredicts surface concentrations of O3 at most sites in nearly all months with NMBs of up to 160.3% and NO3- at the Tsinghua site in all months. Possible reasons for large underpredictions include underestimations in the anthropogenic emissions of CO, SO2, and primary aerosol, inappropriate vertical distributions of emissions of SO2 and NO2, uncertainties in upper boundary conditions (e.g., for O3 and CO), missing or inaccurate model representations (e.g., secondary organic aerosol formation, gas/particle partitioning, dust emissions, dry and wet deposition), and inaccurate meteorological fields (e.g., overpredictions in WS10 and precipitation, but underpredictions in T2), as well as the large uncertainties in satellite retrievals (e.g., for column SO2). Comparing to MM5, WRF generally gives worse performance in meteorological predictions, in particular, T2, WS10, GSW, GLW, and cloud fraction in all months, as well as Q2 and precipitation in January and October, due to limitations in the above physics schemes or parameterizations. Comparing to CMAQ, WRF/Chem performs better for surface CO, O3, and PM10 concentrations at most sites in most months, column CO and SO2 abundances, and AOD. It, however, gives poorer performance for surface NOx concentrations at most sites in most months, surface SO2 concentrations at all sites in all months, and column NO2 abundances in January and April. WRF/Chem also gives lower concentrations of most secondary PM and black carbon. Those differences in results are attributed to differences in simulated meteorology, gas-phase chemistry, aerosol thermodynamic and dynamic treatments, dust and sea salt emissions, and wet and dry deposition treatments in both models.

Multiyear applications of WRF/Chem over continental U.S.: Model evaluation, variation trend, and impacts of boundary conditions

NASA Astrophysics Data System (ADS)

Yahya, Khairunnisa; He, Jian; Zhang, Yang

2015-12-01

Multiyear applications of an online-coupled meteorology-chemistry model allow an assessment of the variation trends in simulated meteorology, air quality, and their interactions to changes in emissions and meteorology, as well as the impacts of initial and boundary conditions (ICONs/BCONs) on simulated aerosol-cloud-radiation interactions over a period of time. In this work, the Weather Research and Forecasting model with Chemistry version 3.4.1 (WRF/Chem v. 3.4.1) with the 2005 Carbon Bond mechanism coupled with the Volatility Basis Set module for secondary organic aerosol formation (WRF/Chem-CB05-VBS) is applied for multiple years (2001, 2006, and 2010) over continental U.S. This work also examines the changes in simulated air quality and meteorology due to changes in emissions and meteorology and the model's capability in reproducing the observed variation trends in species concentrations from 2001 to 2010. In addition, the impacts of the chemical ICONs/BCONs on model predictions are analyzed. ICONs/BCONs are downscaled from two global models, the modified Community Earth System Model/Community Atmosphere model version 5.1 (CESM/CAM v5.1) and the Monitoring Atmospheric Composition and Climate model (MACC). The evaluation of WRF/Chem-CB05-VBS simulations with the CESM ICONs/BCONs for 2001, 2006, and 2010 shows that temperature at 2 m (T2) is underpredicted for all three years likely due to inaccuracies in soil moisture and soil temperature, resulting in biases in surface relative humidity, wind speed, and precipitation. With the exception of cloud fraction, other aerosol-cloud variables including aerosol optical depth, cloud droplet number concentration, and cloud optical thickness are underpredicted for all three years, resulting in overpredictions of radiation variables. The model performs well for O3 and particulate matter with diameter less than or equal to 2.5 (PM2.5) for all three years comparable to other studies from literature. The model is able to reproduce observed annual average trends in O3 and PM2.5 concentrations from 2001 to 2006 and from 2006 to 2010 but is less skillful in simulating their observed seasonal trends. The 2006 and 2010 results using CESM and MACC ICONs/BCONs are compared to analyze the impact of ICONs/BCONs on model performance and their feedbacks to aerosol, clouds, and radiation. Comparing to the simulations with MACC ICONs/BCONs, the simulations with the CESM ICONs/BCONs improve the performance of O3 mixing ratios (e.g., the normalized mean bias for maximum 8 h O3 is reduced from -17% to -1% in 2010), PM2.5 in 2010, and sulfate in 2006 (despite a slightly larger normalized mean bias for PM2.5 in 2006). The impacts of different ICONs/BCONs on simulated aerosol-cloud-radiation variables are not negligible, with larger impacts in 2006 compared to 2010.

Studying the Processes Contributed to the Hairpin Turn of Hurricane Joaquin with WRF numerical simulations and TCI-2015 observations

NASA Astrophysics Data System (ADS)

Pu, Z.; Yu, Y.

2016-12-01

The prediction of Hurricane Joaquin's hairpin clockwise during 1 and 2 October 2015 presents a forecasting challenge during real-time numerical weather prediction, as tracks of several major numerical weather prediction models differ from each other. To investigate the large-scale environment and hurricane inner-core structures related to the hairpin turn of Joaquin, a series of high-resolution mesoscale numerical simulations of Hurricane Joaquin had been performed with an advanced research version of the Weather Research and Forecasting (WRF) model. The outcomes were compared with the observations obtained from the US Office of Naval Research's Tropical Cyclone Intensity (TCI) Experiment during 2015 hurricane season. Specifically, five groups of sensitivity experiments with different cumulus, boundary layer, and microphysical schemes as well as different initial and boundary conditions and initial times in WRF simulations had been performed. It is found that the choice of the cumulus parameterization scheme plays a significant role in reproducing reasonable track forecast during Joaquin's hairpin turn. The mid-level environmental steering flows can be the reason that leads to different tracks in the simulations with different cumulus schemes. In addition, differences in the distribution and amounts of the latent heating over the inner-core region are associated with discrepancies in the simulated intensity among different experiments. Detailed simulation results, comparison with TCI-2015 observations, and comprehensive diagnoses will be presented.

An Enhanced Convective Forecast (ECF) for the New York TRACON Area

NASA Technical Reports Server (NTRS)

Wheeler, Mark; Stobie, James; Gillen, Robert; Jedlovec, Gary; Sims, Danny

2008-01-01

In an effort to relieve summer-time congestion in the NY Terminal Radar Approach Control (TRACON) area, the FAA is testing an enhanced convective forecast (ECF) product. The test began in June 2008 and is scheduled to run through early September. The ECF is updated every two hours, right before the Air Traffic Control System Command Center (ATCSCC) national planning telcon. It is intended to be used by traffic managers throughout the National Airspace System (NAS) and airlines dispatchers to supplement information from the Collaborative Convective Forecast Product (CCFP) and the Corridor Integrated Weather System (CIWS). The ECF begins where the current CIWS forecast ends at 2 hours and extends out to 12 hours. Unlike the CCFP it is a detailed deterministic forecast with no aerial coverage limits. It is created by an ENSCO forecaster using a variety of guidance products including, the Weather Research and Forecast (WRF) model. This is the same version of the WRF that ENSCO runs over the Florida peninsula in support of launch operations at the Kennedy Space Center. For this project, the WRF model domain has been shifted to the Northeastern US. Several products from the NASA SPoRT group are also used by the ENSCO forecaster. In this paper we will provide examples of the ECF products and discuss individual cases of traffic management actions using ECF guidance.

High-Resolution NU-WRF Simulations of a Deep Convective-Precipitation System During MC3E: Part I: Comparisons Between Goddard Microphysics Schemes and Observations

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Wu, Di; Lang, Stephen; Chern, Jiun-Dar; Peters-Lidard, Christa; Fridlind, Ann; Matsui, Toshihisa

2016-01-01

The Goddard microphysics was recently improved by adding a fourth ice class (frozen dropshail). This new 4ICE scheme was developed and tested in the Goddard Cumulus Ensemble (GCE) model for an intense continental squall line and a moderate, less organized continental case. Simulated peak radar reflectivity profiles were improved in intensity and shape for both cases, as were the overall reflectivity probability distributions versus observations. In this study, the new Goddard 4ICE scheme is implemented into the regional-scale NASA Unified-Weather Research and Forecasting (NU-WRF) model, modified and evaluated for the same intense squall line, which occurred during the Midlatitude Continental Convective Clouds Experiment (MC3E). NU-WRF simulated radar reflectivities, total rainfall, propagation, and convective system structures using the 4ICE scheme modified herein agree as well as or significantly better with observations than the original 4ICE and two previous 3ICE (graupel or hail) versions of the Goddard microphysics. With the modified 4ICE, the bin microphysics-based rain evaporation correction improves propagation and in conjunction with eliminating the unrealistic dry collection of icesnow by hail can replicate the erect, narrow, and intense convective cores. Revisions to the ice supersaturation, ice number concentration formula, and snow size mapping, including a new snow breakup effect, allow the modified 4ICE to produce a stronger, better organized system, more snow, and mimic the strong aggregation signature in the radar distributions. NU-WRF original 4ICE simulated radar reflectivity distributions are consistent with and generally superior to those using the GCE due to the less restrictive domain and lateral boundaries.

Interactions between volatile organic compounds and reactive halogen in the tropical marine atmosphere using WRF-Chem

NASA Astrophysics Data System (ADS)

Badia, Alba; Reeves, Claire E.; Baker, Alex; Volkamer, Rainer; von Glasow, Roland

2016-04-01

Halogen species (chlorine, bromine and iodine) are known to play an important role in the chemistry and oxidizing capacity of the troposphere, particularly in the marine boundary layer (MBL). Reactive halogens cause ozone (O3) destruction, change the HOx and NOX partitioning, affect the oxidation of volatile organic compounds (VOCs) and mercury, reduce the lifetime of methane, and take part in new particle formation. Numerical models predicted that reactive halogen compounds account for 30% of O3 destruction in the MBL and 5-20% globally. There are indications that the chemistry of reactive halogens and oxygenated VOCs (OVOCs) in the tropics are inter-related. Moreover, the presence of aldehydes, such as glyoxal (CHOCHO), has a potential impact on radical cycling and secondary organic aerosol (SOA) formation in the MBL and free troposphere (FT). Model calculations suggest aldehydes to be an important sink for bromine atoms and hence competition for their reaction with O3 forming BrO and so illustrating a link between the cycles of halogens and OVOCs in the marine atmosphere. The main objective of this contribution is to investigate the atmospheric chemistry in the tropical East Pacific with a focus on reactive halogens and OVOCs and their links using the latest version of the Weather Research and Forecasting (WRF) model coupled with Chemistry (WRF-Chem) and field data from the TORERO campaign. WRF-Chem is a highly flexible community model for atmospheric research where aerosol-radiation-cloud feedback processes are taken into account. Our current reaction mechanism in WRF-Chem is based on the MOZART mechanism and has been extended to include bromine, chlorine and iodine chemistry. The MOZART mechanism includes detailed gas-phase chemistry of CHOCHO formation as well as state-of-the-science pathways to form SOA. Oceanic emissions of aldehydes, including CHOCHO, and of organic halogens based on measurements from the TORERO campaign have been added into the model. Sea surface emissions of inorganic iodine are calculated using the parameterisation of Carpenter et al., 2013. Focusing on TORERO observations from the ships and a selected number of flights we present an evaluation of the relevant tropospheric gas-phase chemistry (O3, H2O), inorganic halogen species (BrO, IO), aldehydes (CH3CHO, CHOCHO) and Very Short Lived Halocarbons (VSLH).

Statistical Analysis of Atmospheric Forecast Model Accuracy - A Focus on Multiple Atmospheric Variables and Location-Based Analysis

DTIC Science & Technology

2014-04-01

WRF ) model is a numerical weather prediction system designed for operational forecasting and atmospheric research. This report examined WRF model... WRF , weather research and forecasting, atmospheric effects 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT SAR 18. NUMBER OF...and Forecasting ( WRF ) model. The authors would also like to thank Ms. Sherry Larson, STS Systems Integration, LLC, ARL Technical Publishing Branch

Increased Spatial Variability and Intensification of Extreme Monsoon Rainfall due to Urbanization.

PubMed

Paul, Supantha; Ghosh, Subimal; Mathew, Micky; Devanand, Anjana; Karmakar, Subhankar; Niyogi, Dev

2018-03-02

While satellite data provides a strong robust signature of urban feedback on extreme precipitation; urbanization signal is often not so prominent with station level data. To investigate this, we select the case study of Mumbai, India and perform a high resolution (1 km) numerical study with Weather Research and Forecasting (WRF) model for eight extreme rainfall days during 2014-2015. The WRF model is coupled with two different urban schemes, the Single Layer Urban Canopy Model (WRF-SUCM), Multi-Layer Urban Canopy Model (WRF-MUCM). The differences between the WRF-MUCM and WRF-SUCM indicate the importance of the structure and characteristics of urban canopy on modifications in precipitation. The WRF-MUCM simulations resemble the observed distributed rainfall. WRF-MUCM also produces intensified rainfall as compared to the WRF-SUCM and WRF-NoUCM (without UCM). The intensification in rainfall is however prominent at few pockets of urban regions, that is seen in increased spatial variability. We find that the correlation of precipitation across stations within the city falls below statistical significance at a distance greater than 10 km. Urban signature on extreme precipitation will be reflected on station rainfall only when the stations are located inside the urban pockets having intensified precipitation, which needs to be considered in future analysis.

Application of Radioxenon Stack Emission Data in High-Resolution Atmospheric Transport Modelling

NASA Astrophysics Data System (ADS)

Kusmierczyk-Michulec, J.; Schoeppner, M.; Kalinowski, M.; Bourgouin, P.; Kushida, N.; Barè, J.

2017-12-01

The Comprehensive Nuclear-Test-Ban Treaty Organisation (CTBTO) has developed the capability to run high-resolution atmospheric transport modelling by employing WRF and Flexpart-WRF. This new capability is applied to simulate the impact of stack emission data on simulated concentrations and how the availability of such data improves the overall accuracy of atmospheric transport modelling. The presented case study focuses on xenon-133 emissions from IRE, a medical isotope production facility in Belgium, and air concentrations detected at DEX33, a monitoring station close to Freiburg, Germany. The CTBTO is currently monitoring the atmospheric concentration of xenon-133 at 25 stations and will further expand the monitoring efforts to 40 stations worldwide. The incentive is the ability to detect xenon-133 that has been produced and released from a nuclear explosion. A successful detection can be used to prove the nuclear nature of an explosion and even support localization efforts. However, xenon-133 is also released from nuclear power plants and to a larger degree from medical isotope production facilities. The availability of stack emission data in combination with atmospheric transport modelling can greatly facilitate the understanding of xenon-133 concentrations detected at monitoring stations to distinguish between xenon-133 that has been emitted from a nuclear explosion and from civilian sources. Newly available stack emission data is used with a high-resolution version of the Flexpart atmospheric transport model, namely Flexpart-WRF, to assess the impact of the emissions on the detected concentrations and the advantage gained from the availability of such stack emission data. The results are analyzed with regard to spatial and time resolution of the high-resolution model and in comparison to conventional atmospheric transport models with and without stack emission data.

A strategy for representing the effects of convective momentum transport in multiscale models: Evaluation using a new superparameterized version of the Weather Research and Forecast model (SP-WRF)

NASA Astrophysics Data System (ADS)

Tulich, S. N.

2015-06-01

This paper describes a general method for the treatment of convective momentum transport (CMT) in large-scale dynamical solvers that use a cyclic, two-dimensional (2-D) cloud-resolving model (CRM) as a "superparameterization" of convective-system-scale processes. The approach is similar in concept to traditional parameterizations of CMT, but with the distinction that both the scalar transport and diagnostic pressure gradient force are calculated using information provided by the 2-D CRM. No assumptions are therefore made concerning the role of convection-induced pressure gradient forces in producing up or down-gradient CMT. The proposed method is evaluated using a new superparameterized version of the Weather Research and Forecast model (SP-WRF) that is described herein for the first time. Results show that the net effect of the formulation is to modestly reduce the overall strength of the large-scale circulation, via "cumulus friction." This statement holds true for idealized simulations of two types of mesoscale convective systems, a squall line, and a tropical cyclone, in addition to real-world global simulations of seasonal (1 June to 31 August) climate. In the case of the latter, inclusion of the formulation is found to improve the depiction of key synoptic modes of tropical wave variability, in addition to some aspects of the simulated time-mean climate. The choice of CRM orientation is also found to importantly affect the simulated time-mean climate, apparently due to changes in the explicit representation of wide-spread shallow convective regions.

A case study of the Weather Research and Forecasting model applied to the Joint Urban 2003 tracer field experiment. Part 2: Gas tracer dispersion

DOE PAGES

Nelson, Matthew A.; Brown, Michael J.; Halverson, Scot A.; ...

2016-07-28

Here, the Quick Urban & Industrial Complex (QUIC) atmospheric transport, and dispersion modelling, system was evaluated against the Joint Urban 2003 tracer-gas measurements. This was done using the wind and turbulence fields computed by the Weather Research and Forecasting (WRF) model. We compare the simulated and observed plume transport when using WRF-model-simulated wind fields, and local on-site wind measurements. Degradation of the WRF-model-based plume simulations was cased by errors in the simulated wind direction, and limitations in reproducing the small-scale wind-field variability. We explore two methods for importing turbulence from the WRF model simulations into the QUIC system. The firstmore » method uses parametrized turbulence profiles computed from WRF-model-computed boundary-layer similarity parameters; and the second method directly imports turbulent kinetic energy from the WRF model. Using the WRF model’s Mellor-Yamada-Janjic boundary-layer scheme, the parametrized turbulence profiles and the direct import of turbulent kinetic energy were found to overpredict and underpredict the observed turbulence quantities, respectively. Near-source building effects were found to propagate several km downwind. These building effects and the temporal/spatial variations in the observed wind field were often found to have a stronger influence over the lateral and vertical plume spread than the intensity of turbulence. Correcting the WRF model wind directions using a single observational location improved the performance of the WRF-model-based simulations, but using the spatially-varying flow fields generated from multiple observation profiles generally provided the best performance.« less

A case study of the Weather Research and Forecasting model applied to the Joint Urban 2003 tracer field experiment. Part 2: Gas tracer dispersion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nelson, Matthew A.; Brown, Michael J.; Halverson, Scot A.

Here, the Quick Urban & Industrial Complex (QUIC) atmospheric transport, and dispersion modelling, system was evaluated against the Joint Urban 2003 tracer-gas measurements. This was done using the wind and turbulence fields computed by the Weather Research and Forecasting (WRF) model. We compare the simulated and observed plume transport when using WRF-model-simulated wind fields, and local on-site wind measurements. Degradation of the WRF-model-based plume simulations was cased by errors in the simulated wind direction, and limitations in reproducing the small-scale wind-field variability. We explore two methods for importing turbulence from the WRF model simulations into the QUIC system. The firstmore » method uses parametrized turbulence profiles computed from WRF-model-computed boundary-layer similarity parameters; and the second method directly imports turbulent kinetic energy from the WRF model. Using the WRF model’s Mellor-Yamada-Janjic boundary-layer scheme, the parametrized turbulence profiles and the direct import of turbulent kinetic energy were found to overpredict and underpredict the observed turbulence quantities, respectively. Near-source building effects were found to propagate several km downwind. These building effects and the temporal/spatial variations in the observed wind field were often found to have a stronger influence over the lateral and vertical plume spread than the intensity of turbulence. Correcting the WRF model wind directions using a single observational location improved the performance of the WRF-model-based simulations, but using the spatially-varying flow fields generated from multiple observation profiles generally provided the best performance.« less

Sensitivity of WRF precipitation on microphysical and boundary layer parameterizations during extreme events in Eastern Mediterranean

NASA Astrophysics Data System (ADS)

Pytharoulis, I.; Karagiannidis, A. F.; Brikas, D.; Katsafados, P.; Papadopoulos, A.; Mavromatidis, E.; Kotsopoulos, S.; Karacostas, T. S.

2010-09-01

Contemporary atmospheric numerical models contain a large number of physical parameterization schemes in order to represent the various atmospheric processes that take place in sub-grid scales. The choice of the proper combination of such schemes is a challenging task for research and particularly for operational purposes. This choice becomes a very important decision in cases of high impact weather in which the forecast errors and the concomitant societal impacts are expected to be large. Moreover, it is well known that one of the hardest tasks for numerical models is to predict precipitation with a high degree of accuracy. The use of complex and sophisticated schemes usually requires more computational time and resources, but it does not necessarily lead to better forecasts. The aim of this study is to investigate the sensitivity of the model predicted precipitation on the microphysical and boundary layer parameterizations during extreme events. The nonhydrostatic Weather Research and Forecasting model with the Advanced Research dynamic solver (WRF-ARW Version 3.1.1) is utilized. It is a flexible, state-of-the-art numerical weather prediction system designed to operate in both research and operational mode in global and regional scales. Nine microphysical and two boundary layer schemes are combined in the sensitivity experiments. The 9 microphysical schemes are: i) Lin, ii) WRF Single Moment 5-classes, iii) Ferrier new Eta, iv) WRF Single Moment 6-classes, v) Goddard, vi) New Thompson V3.1, vii) WRF Double Moment 5-classes, viii) WRF Double Moment 6-classes, ix) Morrison. The boundary layer is parameterized using the schemes of: i) Mellor-Yamada-Janjic (MYJ) and ii) Mellor-Yamada-Nakanishi-Niino (MYNN) level 2.5. The model is integrated at very high horizontal resolution (2 km x 2 km in the area of interest) utilizing 38 vertical levels. Three cases of high impact weather in Eastern Mediterranean, associated with strong synoptic scale forcing, are employed in the numerical experiments. These events are characterized by strong precipitation with daily amounts exceeding 100 mm. For example, the case of 24 to 26 October 2009 was associated with floods in the eastern mainland of Greece. In Pieria (northern Greece), that was the most afflicted area, one individual perished in the overflowed Esonas river and significant damages were caused in both the infrastructure and cultivations. Precipitation amounts of 347 mm in 3 days were measured in the station of Vrontou, Pieria (which is at an elevation of only 120 m). The model results are statistically analysed and compared to the available surface observations and satellite derived precipitation data in order to identify the parameterizations (and their combinations) that provide the best representation of the spatiotemporal variability of precipitation in extreme conditions. Preliminary results indicate that the MYNN boundary layer parameterization outperforms the one of MYJ. However, the best results are produced by the combination of the Ferrier new Eta microphysics with the MYJ scheme, which are the default schemes of the well-known and reliable ETA and WRF-NMM models. Similarly, good results are produced by the combination of the New Thompson V3.1 microphysics with MYNN boundary layer scheme. On the other hand, the worst results (with mean absolute error up to about 150 mm/day) appear when the WRF Single Moment 5-classes scheme is used with MYJ. Finally, an effort is made to identify and analyze the main factors that are responsible for the aforementioned differences.

Investigating Surface Bias Errors in the Weather Research and Forecasting (WRF) Model using a Geographic Information System (GIS)

DTIC Science & Technology

2015-02-01

WRF ) Model using a Geographic Information System (GIS) by Jeffrey A Smith, Theresa A Foley, John W Raby, and Brian Reen...ARL-TR-7212 ● FEB 2015 US Army Research Laboratory Investigating Surface Bias Errors in the Weather Research and Forecasting ( WRF ) Model...SUBTITLE Investigating surface bias errors in the Weather Research and Forecasting ( WRF ) Model using a Geographic Information System (GIS) 5a

Studies regarding the quality of numerical weather forecasts of the WRF model integrated at high-resolutions for the Romanian territory

DOE PAGES

Iriza, Amalia; Dumitrache, Rodica C.; Lupascu, Aurelia; ...

2016-01-01

Our paper aims to evaluate the quality of high-resolution weather forecasts from the Weather Research and Forecasting (WRF) numerical weather prediction model. The lateral and boundary conditions were obtained from the numerical output of the Consortium for Small-scale Modeling (COSMO) model at 7 km horizontal resolution. Furthermore, the WRF model was run for January and July 2013 at two horizontal resolutions (3 and 1 km). The numerical forecasts of the WRF model were evaluated using different statistical scores for 2 m temperature and 10 m wind speed. Our results showed a tendency of the WRF model to overestimate the valuesmore » of the analyzed parameters in comparison to observations.« less

Studies regarding the quality of numerical weather forecasts of the WRF model integrated at high-resolutions for the Romanian territory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Iriza, Amalia; Dumitrache, Rodica C.; Lupascu, Aurelia

Our paper aims to evaluate the quality of high-resolution weather forecasts from the Weather Research and Forecasting (WRF) numerical weather prediction model. The lateral and boundary conditions were obtained from the numerical output of the Consortium for Small-scale Modeling (COSMO) model at 7 km horizontal resolution. Furthermore, the WRF model was run for January and July 2013 at two horizontal resolutions (3 and 1 km). The numerical forecasts of the WRF model were evaluated using different statistical scores for 2 m temperature and 10 m wind speed. Our results showed a tendency of the WRF model to overestimate the valuesmore » of the analyzed parameters in comparison to observations.« less

Effects of downscaled high-resolution meteorological data on the PSCF identification of emission sources

DOE PAGES

Cheng, Meng -Dawn; Kabela, Erik D.

2016-04-30

The Potential Source Contribution Function (PSCF) model has been successfully used for identifying regions of emission source at a long distance in this study, the PSCF model relies on backward trajectories calculated by the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. In this study, we investigated the impacts of grid resolution and Planetary Boundary Layer (PBL) parameterization (e.g., turbulent transport of pollutants) on the PSCF analysis. The Mellor-Yamada-Janjic (MYJ) and Yonsei University (YUS) parameterization schemes were selected to model the turbulent transport in the PBL within the Weather Research and Forecasting (WRF version 3.6) model. Two separate domain grid sizesmore » (83 and 27 km) were chosen in the WRF downscaling in generating the wind data for driving the HYSPLIT calculation. The effects of grid size and PBL parameterization are important in incorporating the influ- ence of regional and local meteorological processes such as jet streaks, blocking patterns, Rossby waves, and terrain-induced convection on the transport of pollutants by a wind trajectory. We found high resolution PSCF did discover and locate source areas more precisely than that with lower resolution meteorological inputs. The lack of anticipated improvement could also be because a PBL scheme chosen to produce the WRF data was only a local parameterization and unable to faithfully duplicate the real atmosphere on a global scale. The MYJ scheme was able to replicate PSCF source identification by those using the Reanalysis and discover additional source areas that was not identified by the Reanalysis data. In conclusion, a potential benefit for using high-resolution wind data in the PSCF modeling is that it could discover new source location in addition to those identified by using the Reanalysis data input.« less

Weather Research and Forecasting Model with Vertical Nesting Capability

DOE Office of Scientific and Technical Information (OSTI.GOV)

2014-08-01

The Weather Research and Forecasting (WRF) model with vertical nesting capability is an extension of the WRF model, which is available in the public domain, from www.wrf-model.org. The new code modifies the nesting procedure, which passes lateral boundary conditions between computational domains in the WRF model. Previously, the same vertical grid was required on all domains, while the new code allows different vertical grids to be used on concurrently run domains. This new functionality improves WRF's ability to produce high-resolution simulations of the atmosphere by allowing a wider range of scales to be efficiently resolved and more accurate lateral boundarymore » conditions to be provided through the nesting procedure.« less

Toward Surface Mass Balance Modeling over Antarctic Peninsula with Improved Snow/Ice Physics within WRF

NASA Astrophysics Data System (ADS)

Villamil-Otero, G.; Zhang, J.; Yao, Y.

2017-12-01

The Antarctic Peninsula (AP) has long been the focus of climate change studies due to its rapid environmental changes such as significantly increased glacier melt and retreat, and ice-shelf break-up. Progress has been continuously made in the use of regional modeling to simulate surface mass changes over ice sheets. Most efforts, however, focus on the ice sheets of Greenland with considerable fewer studies in Antarctica. In this study the Weather Research and Forecasting (WRF) model, which has been applied to the Antarctic region for weather modeling, is adopted to capture the past and future surface mass balance changes over AP. In order to enhance the capabilities of WRF model simulating surface mass balance over the ice surface, we implement various ice and snow processes within the WRF and develop a new WRF suite (WRF-Ice). The WRF-Ice includes a thermodynamic ice sheet model that improves the representation of internal melting and refreezing processes and the thermodynamic effects over ice sheet. WRF-Ice also couples a thermodynamic sea ice model to improve the simulation of surface temperature and fluxes over sea ice. Lastly, complex snow processes are also taken into consideration including the implementation of a snowdrift model that takes into account the redistribution of blowing snow as well as the thermodynamic impact of drifting snow sublimation on the lower atmospheric boundary layer. Intensive testing of these ice and snow processes are performed to assess the capability of WRF-Ice in simulating the surface mass balance changes over AP.

Effect of different emission inventories on modeled ozone and carbon monoxide in Southeast Asia

NASA Astrophysics Data System (ADS)

Amnuaylojaroen, T.; Barth, M. C.; Emmons, L. K.; Carmichael, G. R.; Kreasuwun, J.; Prasitwattanaseree, S.; Chantara, S.

2014-04-01

In order to improve our understanding of air quality in Southeast Asia, the anthropogenic emissions inventory must be well represented. In this work, we apply different anthropogenic emission inventories in the Weather Research and Forecasting Model with Chemistry (WRF-Chem) version 3.3 using MOZART gas-phase chemistry and GOCART aerosols to examine the differences in predicted carbon monoxide (CO) and ozone (O3) surface mixing ratios for Southeast Asia in March and December 2008. The anthropogenic emission inventories include the Reanalysis of the TROpospheric chemical composition (RETRO), the Intercontinental Chemical Transport Experiment-Phase B (INTEX-B), the MACCity emissions (adapted from the Monitoring Atmospheric Composition and Climate and megacity Zoom for the Environment projects), the Southeast Asia Composition, Cloud, Climate Coupling Regional Study (SEAC4RS) emissions, and a combination of MACCity and SEAC4RS emissions. Biomass burning emissions are from the Fire Inventory from NCAR (FINNv1) model. WRF-chem reasonably predicts the 2 m temperature, 10 m wind, and precipitation. In general, surface CO is underpredicted by WRF-Chem while surface O3 is overpredicted. The NO2 tropospheric column predicted by WRF-Chem has the same magnitude as observations, but tends to underpredict NO2 column over the equatorial ocean and near Indonesia. Simulations using different anthropogenic emissions produce only a slight variability of O3 and CO mixing ratios, while biomass burning emissions add more variability. The different anthropogenic emissions differ by up to 20% in CO emissions, but O3 and CO mixing ratios differ by ~4.5% and ~8%, respectively, among the simulations. Biomass burning emissions create a substantial increase for both O3 and CO by ~29% and ~16%, respectively, when comparing the March biomass burning period to December with low biomass burning emissions. The simulations show that none of the anthropogenic emission inventories are better than the others and any of the examined inventories can be used for air quality simulations in Southeast Asia.

The 2010 Pakistan floods: high-resolution simulations with the WRF model

NASA Astrophysics Data System (ADS)

Viterbo, Francesca; Parodi, Antonio; Molini, Luca; Provenzale, Antonello; von Hardenberg, Jost; Palazzi, Elisa

2013-04-01

Estimating current and future water resources in high mountain regions with complex orography is a difficult but crucial task. In particular, the French-Italian project PAPRIKA is focused on two specific regions in the Hindu-Kush -- Himalaya -- Karakorum (HKKH)region: the Shigar basin in Pakistan, at the feet of K2, and the Khumbu valley in Nepal, at the feet of Mount Everest. In this framework, we use the WRF model to simulate precipitation and meteorological conditions with high resolution in areas with extreme orographic slopes, comparing the model output with station and satellite data. Once validated the model, we shall run a set of three future time-slices at very high spatial resolution, in the periods 2046-2050, 2071-2075 and 2096-2100, nested in different climate change scenarios (EXtreme PREcipitation and Hydrological climate Scenario Simulations -EXPRESS-Hydro project). As a prelude to this study, here we discuss the simulation of specific, high-intensity rainfall events in this area. In this paper we focus on the 2010 Pakistan floods which began in late July 2010, producing heavy monsoon rains in the Khyber Pakhtunkhwa, Sindh, Punjab and Balochistan regions of Pakistan and affecting the Indus River basin. Approximately one-fifth of Pakistan's total land area was underwater, with a death toll of about 2000 people. This event has been simulated with the WRF model (version 3.3.) in cloud-permitting mode (d01 14 km and d02 3.5 km): different convective closures and microphysics parameterization have been used. A deeper understanding of the processes responsible for this event has been gained through comparison with rainfall depth observations, radiosounding data and geostationary/polar satellite images.

Comparative Evaluation of the Impact of WRF-NMM and WRF-ARW Meteorology on CMAQ Simulations for O3 and Related Species During the 2006 TexAQS/GoMACCS Campaign

EPA Science Inventory

In this paper, impact of meteorology derived from the Weather, Research and Forecasting (WRF)– Non–hydrostatic Mesoscale Model (NMM) and WRF–Advanced Research WRF (ARW) meteorological models on the Community Multiscale Air Quality (CMAQ) simulations for ozone and its related prec...

Full Coupling Between the Atmosphere, Surface, and Subsurface for Integrated Hydrologic Simulation

NASA Astrophysics Data System (ADS)

Davison, Jason Hamilton; Hwang, Hyoun-Tae; Sudicky, Edward A.; Mallia, Derek V.; Lin, John C.

2018-01-01

An ever increasing community of earth system modelers is incorporating new physical processes into numerical models. This trend is facilitated by advancements in computational resources, improvements in simulation skill, and the desire to build numerical simulators that represent the water cycle with greater fidelity. In this quest to develop a state-of-the-art water cycle model, we coupled HydroGeoSphere (HGS), a 3-D control-volume finite element surface and variably saturated subsurface flow model that includes evapotranspiration processes, to the Weather Research and Forecasting (WRF) Model, a 3-D finite difference nonhydrostatic mesoscale atmospheric model. The two-way coupled model, referred to as HGS-WRF, exchanges the actual evapotranspiration fluxes and soil saturations calculated by HGS to WRF; conversely, the potential evapotranspiration and precipitation fluxes from WRF are passed to HGS. The flexible HGS-WRF coupling method allows for unique meshes used by each model, while maintaining mass and energy conservation between the domains. Furthermore, the HGS-WRF coupling implements a subtime stepping algorithm to minimize computational expense. As a demonstration of HGS-WRF's capabilities, we applied it to the California Basin and found a strong connection between the depth to the groundwater table and the latent heat fluxes across the land surface.

Joint atmospheric-terrestrial water balances for East Africa: a WRF-Hydro case study for the upper Tana River basin

NASA Astrophysics Data System (ADS)

Kerandi, Noah; Arnault, Joel; Laux, Patrick; Wagner, Sven; Kitheka, Johnson; Kunstmann, Harald

2018-02-01

For an improved understanding of the hydrometeorological conditions of the Tana River basin of Kenya, East Africa, its joint atmospheric-terrestrial water balances are investigated. This is achieved through the application of the Weather Research and Forecasting (WRF) and the fully coupled WRF-Hydro modeling system over the Mathioya-Sagana subcatchment (3279 km2) and its surroundings in the upper Tana River basin for 4 years (2011-2014). The model setup consists of an outer domain at 25 km (East Africa) and an inner one at 5-km (Mathioya-Sagana subcatchment) horizontal resolution. The WRF-Hydro inner domain is enhanced with hydrological routing at 500-m horizontal resolution. The results from the fully coupled modeling system are compared to those of the WRF-only model. The coupled WRF-Hydro slightly reduces precipitation, evapotranspiration, and the soil water storage but increases runoff. The total precipitation from March to May and October to December for WRF-only (974 mm/year) and coupled WRF-Hydro (940 mm/year) is closer to that derived from the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data (989 mm/year) than from the TRMM (795 mm/year) precipitation product. The coupled WRF-Hydro-accumulated discharge (323 mm/year) is close to that observed (333 mm/year). However, the coupled WRF-Hydro underestimates the observed peak flows registering low but acceptable NSE (0.02) and RSR (0.99) at daily time step. The precipitation recycling and efficiency measures between WRF-only and coupled WRF-Hydro are very close and small. This suggests that most of precipitation in the region comes from moisture advection from the outside of the analysis domain, indicating a minor impact of potential land-precipitation feedback mechanisms in this case. The coupled WRF-Hydro nonetheless serves as a tool in quantifying the atmospheric-terrestrial water balance in this region.

WRF-Chem simulations in the Amazon region during wet and dry season transitions: evaluation of methane models and wetland inundation maps

NASA Astrophysics Data System (ADS)

Beck, V.; Gerbig, C.; Koch, T.; Bela, M. M.; Longo, K. M.; Freitas, S. R.; Kaplan, J. O.; Prigent, C.; Bergamaschi, P.; Heimann, M.

2013-08-01

The Amazon region, being a large source of methane (CH4), contributes significantly to the global annual CH4 budget. For the first time, a forward and inverse modelling framework on regional scale for the purpose of assessing the CH4 budget of the Amazon region is implemented. Here, we present forward simulations of CH4 as part of the forward and inverse modelling framework based on a modified version of the Weather Research and Forecasting model with chemistry that allows for passive tracer transport of CH4, carbon monoxide, and carbon dioxide (WRF-GHG), in combination with two different process-based bottom-up models of CH4 emissions from anaerobic microbial production in wetlands and additional datasets prescribing CH4 emissions from other sources such as biomass burning, termites, or other anthropogenic emissions. We compare WRF-GHG simulations on 10 km horizontal resolution to flask and continuous CH4 observations obtained during two airborne measurement campaigns within the Balanço Atmosférico Regional de Carbono na Amazônia (BARCA) project in November 2008 and May 2009. In addition, three different wetland inundation maps, prescribing the fraction of inundated area per grid cell, are evaluated. Our results indicate that the wetland inundation maps based on remote-sensing data represent the observations best except for the northern part of the Amazon basin and the Manaus area. WRF-GHG was able to represent the observed CH4 mixing ratios best at days with less convective activity. After adjusting wetland emissions to match the averaged observed mixing ratios of flights with little convective activity, the monthly CH4 budget for the Amazon basin obtained from four different simulations ranges from 1.5 to 4.8 Tg for November 2008 and from 1.3 to 5.5 Tg for May 2009. This corresponds to an average CH4 flux of 9-31 mg m-2 d-1 for November 2008 and 8-36 mg m-2 d-1 for May 2009.

Extending flood forecasting lead time in a large watershed by coupling WRF QPF with a distributed hydrological model

NASA Astrophysics Data System (ADS)

Li, Ji; Chen, Yangbo; Wang, Huanyu; Qin, Jianming; Li, Jie; Chiao, Sen

2017-03-01

Long lead time flood forecasting is very important for large watershed flood mitigation as it provides more time for flood warning and emergency responses. The latest numerical weather forecast model could provide 1-15-day quantitative precipitation forecasting products in grid format, and by coupling this product with a distributed hydrological model could produce long lead time watershed flood forecasting products. This paper studied the feasibility of coupling the Liuxihe model with the Weather Research and Forecasting quantitative precipitation forecast (WRF QPF) for large watershed flood forecasting in southern China. The QPF of WRF products has three lead times, including 24, 48 and 72 h, with the grid resolution being 20 km  × 20 km. The Liuxihe model is set up with freely downloaded terrain property; the model parameters were previously optimized with rain gauge observed precipitation, and re-optimized with the WRF QPF. Results show that the WRF QPF has bias with the rain gauge precipitation, and a post-processing method is proposed to post-process the WRF QPF products, which improves the flood forecasting capability. With model parameter re-optimization, the model's performance improves also. This suggests that the model parameters be optimized with QPF, not the rain gauge precipitation. With the increasing of lead time, the accuracy of the WRF QPF decreases, as does the flood forecasting capability. Flood forecasting products produced by coupling the Liuxihe model with the WRF QPF provide a good reference for large watershed flood warning due to its long lead time and rational results.

Investigation of polar mesocyclones in Arctic Ocean using COSMO-CLM and WRF numerical models and remote sensing data

NASA Astrophysics Data System (ADS)

Varentsov, Mikhail; Verezemskaya, Polina; Baranyuk, Anastasia; Zabolotskikh, Elizaveta; Repina, Irina

2015-04-01

Polar lows (PL), high latitude marine mesoscale cyclones, are an enigmatic atmospheric phenomenon, which could result in windstorm damage of shipping and infrastructure in high latitudes. Because of their small spatial scales, short life times and their tendency to develop in remote data sparse regions (Zahn, Strorch, 2008), our knowledge of their behavior and climatology lags behind that of synoptic-scale cyclones. In case of continuing global warming (IPCC, 2013) and prospects of the intensification of economic activity and marine traffic in Arctic region, the problem of relevant simulation of this phenomenon by numerical models of the atmosphere, which could be used for weather and climate prediction, is especially important. The focus of this paper is researching the ability to simulate polar lows by two modern nonhydrostatic mesoscale numerical models, driven by realistic lateral boundary conditions from ERA-Interim reanalysis: regional climate model COSMO-CLM (Böhm et. al., 2009) and weather prediction and research model (WRF). Fields of wind, pressure and cloudiness, simulated by models, were compared with remote sensing data and ground meteorological observations for several cases, when polar lows were observed, in Norwegian, Kara and Laptev seas. Several types of satellite data were used: atmospheric water vapor, cloud liquid water content and surface wind fields were resampled by examining AMSR-E and AMSR-2 microwave radiometer data (MODIS Aqua, GCOM-W1), and wind fields were additionally extracted from QuickSCAT scatterometer. Infrared and visible pictures of cloud cover were obtained from MODIS (Aqua). Completed comparison shown that COSMO-CLM and WRF models could successfully reproduce evolution of polar lows and their most important characteristics such as size and wind speed in short experiments with WRF model and longer (up to half-year) experiments with COSMO-CLM model. Improvement of the quality of polar lows reproduction by these models in relation to source reanalysis fields were investigated. References: 1. Böhm U. et al. CLM - the climate version of LM: Brief description and long-term applications [Journal] // COSMO Newsletter. - 2006. - Vol. 6. 2. IPCC Fifth Assessment Report: Climate Change 2013 (AR5) Rep.,Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. 3. Zahn, M., and H. von Storch (2008), A long-term climatology of North Atlantic polar lows, Geophys. Res. Lett., 35, L22702

Applying the WRF Double-Moment Six-Class Microphysics Scheme in the GRAPES_Meso Model: A Case Study

NASA Astrophysics Data System (ADS)

Zhang, Meng; Wang, Hong; Zhang, Xiaoye; Peng, Yue; Che, Huizheng

2018-04-01

This study incorporated the Weather Research and Forecasting (WRF) model double-moment 6-class (WDM6) microphysics scheme into the mesoscale version of the Global/Regional Assimilation and PrEdiction System (GRAPES_Meso). A rainfall event that occurred during 3-5 June 2015 around Beijing was simulated by using the WDM6, the WRF single-moment 6-class scheme (WSM6), and the NCEP 5-class scheme, respectively. The results show that both the distribution and magnitude of the rainfall simulated with WDM6 were more consistent with the observation. Compared with WDM6, WSM6 simulated larger cloud liquid water content, which provided more water vapor for graupel growth, leading to increased precipitation in the cold-rain processes. For areas with the warmrain processes, the sensitivity experiments using WDM6 showed that an increase in cloud condensation nuclei (CCN) number concentration led to enhanced CCN activation ratio and larger cloud droplet number concentration ( N c) but decreased cloud droplet effective diameter. The formation of more small-size cloud droplets resulted in a decrease in raindrop number concentration ( N r), inhibiting the warm-rain processes, thus gradually decreasing the amount of precipitation. For areas mainly with the cold-rain processes, the overall amount of precipitation increased; however, it gradually decreased when the CCN number concentration reached a certain magnitude. Hence, the effect of CCN number concentration on precipitation exhibits significant differences in different rainfall areas of the same precipitation event.

Setting up an atmospheric-hydrologic model for seasonal forecasts of water flow into dams in a mountainous semi-arid environment (Cyprus)

NASA Astrophysics Data System (ADS)

Camera, Corrado; Bruggeman, Adriana; Zittis, Georgios; Hadjinicolaou, Panos

2017-04-01

Due to limited rainfall concentrated in the winter months and long dry summers, storage and management of water resources is of paramount importance in Cyprus. For water storage purposes, the Cyprus Water Development Department is responsible for the operation of 56 large dams total volume of 310 Mm3) and 51 smaller reservoirs (total volume of 17 Mm3) over the island. Climate change is also expected to heavily affect Cyprus water resources with a 1.5%-12% decrease in mean annual rainfall (Camera et al., 2016) projected for the period 2020-2050, relative to 1980-2010. This will make reliable seasonal water inflow forecasts even more important for water managers. The overall aim of this study is to set-up the widely used Weather Research and Forecasting (WRF) model with its hydrologic extension (WRF-hydro), for seasonal forecasts of water inflow in dams located in the Troodos Mountains of Cyprus. The specific objectives of this study are: i) the calibration and evaluation of WRF-Hydro for the simulation of stream flows, in the Troodos Mountains, for past rainfall seasons; ii) a sensitivity analysis of the model parameters; iii) a comparison of the application of the atmospheric-hydrologic modelling chain versus the use of climate observations as forcing. The hydrologic model is run in its off-line version with daily forcing over a 1-km grid, while the overland and channel routing is performed on a 100-m grid with a time-step of 6 seconds. Model outputs are exported on a daily base. First, WRF-Hydro is calibrated and validated over two 1-year periods (October-September), using a 1-km gridded observational precipitation dataset (Camera et al., 2014) as input. For the calibration and validation periods, years with annual rainfall close to the long-term average and with the presence of extreme rainfall and flow events were selected. A sensitivity analysis is performed, for the following parameters: partitioning of rainfall into runoff and infiltration (REFKDT), the partitioning of deep percolation between losses and baseflow contribution (LOSS_BASE), water retention depth (RETDEPRTFAC), overland roughness (OVROUGHRTFAC), and channel manning coefficients (MANN). The calibrated WRF-Hydro shows a good ability to reproduce annual total streamflow (-19% error) and total peak discharge volumes (+3% error), although very high values of MANN were used to match the timing of the peak and get positive values of Nash-Sutcliffe efficiency coefficient (0.13). The two most sensitive parameters for the modeled seasonal flow were REFKDT and LOSS_BASE. Simulations of the calibrated WRF-Hydro with WRF modelled atmospheric forcing showed high errors in comparison with those forced with observations, which can be corrected only by modifying the most sensitive parameters by at least one order of magnitude. This study has received funding from the EU H2020 BINGO Project (GA 641739). Camera C., Bruggeman A., Hadjinicolaou P., Pashiardis S., Lange M.A., 2016. Evaluation of interpolation techniques for the creation of gridded daily precipitation (1 × 1 km2); Cyprus, 1980-2010. J Geophys Res Atmos 119, 693-712, DOI:10.1002/2013JD020611 Camera C., Bruggeman A., Hadjinicolaou P., Michaelides S., Lange M.A., 2016. Evaluation of a spatial rainfall generator for generating high resolution precipitation projections over orographically complex terrain. Stoch Environ Res Risk Assess, DOI 10.1007/s00477-016-1239-1

WRF-Fire: coupled weather-wildland fire modeling with the weather research and forecasting model

Treesearch

Janice L. Coen; Marques Cameron; John Michalakes; Edward G. Patton; Philip J. Riggan; Kara M. Yedinak

2012-01-01

A wildland fire behavior module (WRF-Fire) was integrated into the Weather Research and Forecasting (WRF) public domain numerical weather prediction model. The fire module is a surface fire behavior model that is two-way coupled with the atmospheric model. Near-surface winds from the atmospheric model are interpolated to a finer fire grid and used, with fuel properties...

Air quality modelling over the Eastern Mediterranean using the WRF/Chem model: Comparison of gas-phase chemistry and aerosol mechanisms

NASA Astrophysics Data System (ADS)

Georgiou, George K.; Christoudias, Theodoros; Proestos, Yiannis; Kushta, Jonilda; Hadjinicolaou, Panos; Lelieveld, Jos

2017-04-01

A comprehensive analysis of the performance of three coupled gas-phase chemistry and aerosol mechanisms included in the WRF/Chem model has been performed over the Eastern Mediterranean focusing on Cyprus during the CYPHEX campaign in 2014, using high temporal and spatial resolution. The model performance was evaluated by comparing calculations to measurements of gas phase species (O3, CO, NOx, SO2) and aerosols (PM10, PM2.5) from 13 ground stations. Initial results indicate that the calculated day-to-day and diurnal variations of the aforementioned species show good agreement with observations. The model was set up with three nested grids, downscaling to 4km over Cyprus. The meteorological boundary conditions were updated every 3 hours throughout the simulation using the Global Forecast System (GFS), while chemical boundary conditions were updated every 6 hours using the MOZART global chemical transport model. Biogenic emissions were calculated online by the the Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1). Anthropogenic emissions were based on the EDGAR HTAP v2 global emission inventory, provided on a horizontal grid resolution of 0.1o × 0.1o. Three simulations were performed employing different chemistry and aerosol mechanisms; i) RADM2 chemical mechanism and MADE/SORGAM aerosols, ii) CBMZ chemical mechanism and MOSAIC aerosols, iii) MOZART chemical mechanism and MOSAIC aerosols. Results show that the WRF/Chem model satisfactorily estimates the trace gases relative concentrations at the background sites but not at the urban and traffic sites, while some differences appear between the simulated concentrations by the three mechanisms. The resulting discrepancies between the model outcome and measurements, especially at the urban and traffic sites, suggest that a higher resolution anthropogenic emission inventory might help improve fine resolution, regional air quality modelling. Differences in the simulated concentrations by the three chemical mechanisms are attributed to the different chemical species and reaction rate constants used.

Projected climatic changes on drought conditions over Spain

NASA Astrophysics Data System (ADS)

García-Valdecasas Ojeda, Matilde; Quishpe-Vásquez, César; Raquel Gámiz-Fortis, Sonia; Castro-Díez, Yolanda; Jesús Esteban-Parra, María

2017-04-01

In a context of global warming, the evapotranspiration processes will have a strong influence on drought severity. For this reason, the Standardized Precipitation Evapotranspiration Index (SPEI) was computed at different timescales in order to explore the projected drought changes for the main watersheds in Spain. For that, the Weather Research and Forecasting (WRF) model has been used in order to obtain current (1980-2010) and future (2021-2050 and 2071-2100) climate output fields. WRF model was used over a domain that spans the Iberian Peninsula with a spatial resolution of 0.088°, and nested in the coarser 0.44° EURO-CORDEX domain, and driving by the global bias-corrected climate model output data from version 1 of NCAR's Community Earth System Model (CESM1), using two different Representative Concentration Pathway (RCP) scenarios: RCP 4.5 and RCP 8.5. Besides, to examine the behavior of this drought index, a comparison with the Standardized Precipitation Index (SPI), which does not consider the evapotranspiration effects, was also performed. Additionally the relationship between the SPEI index and the soil moisture has also been analyzed. The results of this study suggest an increase in the severity and duration of drought, being larger when the SPEI index is used to define drought events. This fact confirms the relevance of taking into account the evapotranspiration processes to detect future drought events. The results also show a noticeable relationship between the SPEI and the simulated soil moisture content, which is more significant at higher timescales. Keywords: Drought, SPEI, SPI, Climatic change, Projections, WRF. Acknowledgements: This work has been financed by the projects P11-RNM-7941 (Junta de Andalucía-Spain) and CGL2013-48539-R (MINECO-Spain, FEDER).

High Resolution Simulation of a Colorado Rockies Extreme Snow and Rain Event in both a Current and Future Climate

NASA Astrophysics Data System (ADS)

Rasmussen, Roy; Ikeda, Kyoko; Liu, Changhai; Gutmann, Ethan; Gochis, David

2016-04-01

Modeling of extreme weather events often require very finely resolved treatment of atmospheric circulation structures in order to produce and localize the large moisture fluxes that result in extreme precipitation. This is particularly true for cool season orographic precipitation processes where the representation of the landform can significantly impact vertical velocity profiles and cloud moisture entrainment rates. This study presents results for high resolution regional climate modeling study of the Colorado Headwaters region using an updated version of the Weather Research and Forecasting (WRF) model run at 4 km horizontal resolution and a hydrological extension package called WRF-Hydro. Previous work has shown that the WRF modeling system can produce credible depictions of winter orographic precipitation over the Colorado Rockies if run at horizontal resolutions < 6 km. Here we present results from a detailed study of an extreme springtime snowfall event that occurred along the Colorado Front Range in March 2003. Results from the impact of warming on total precipitation, snow-rain partitioning and surface hydrological fluxes (evapotranspiration and runoff) will be discussed in the context of how potential changes in temperature impact the amount of precipitation, the phase of precipitation (rain vs. snow) and the timing and amplitude of streamflow responses. The results show using the Pseudo Global Warming technique that intense precipitation rates significantly increased during the event and a significant fraction of the snowfall converts to rain which significantly amplifies the runoff response from one where runoff is produced gradually to one in which runoff is rapidly translated into streamflow values that approach significant flooding risks. Results from a new, CONUS scale high resolution climate simulation of extreme events in a current and future climate will be presented as time permits.

Influence of boundary-layer dynamics on pollen dispersion and viability

NASA Astrophysics Data System (ADS)

Arritt, Raymond W.; Viner, Brian J.; Westgate, Mark E.

2013-04-01

Adoption of genetically modified (GM) crops has raised concerns that GM traits can accidentally cross into conventional crops or wild relatives through the transport of wind-borne pollen. In order to evaluate this risk it is necessary to account both for dispersion of the pollen grains and environmental influences on pollen viability. The Lagrangian approach is suited to this problem because it allows tracking the environmental temperature and moisture that pollen grains experience as they travel. Taking advantage of this capability we have combined a high-resolution version of the WRF meteorological model with a Lagrangian particle dispersion model to predict maize pollen dispersion and viability. WRF is used to obtain fields of wind, turbulence kinetic energy, temperature, and humidity which are then used as input to the Lagrangian dispersion model. The dispersion model in turn predicts transport of a statistical sample of a pollen cloud from source plants to receptors. We also use the three-dimensional temperature and moisture fields from WRF to diagnose changes in moisture content of the pollen grains and consequent loss of viability. Results show that turbulent motions in the convective boundary layer counteract the large terminal velocity of maize pollen grains and lift them to heights of several hundred meters, so that they can be transported long distances before settling to the ground. We also found that pollen lifted into the upper part of the boundary layer remains more viable than has been inferred using surface observations of temperature and humidity. This is attributed to the thermal and moisture structure that typifies the daytime atmospheric boundary layer, producing an environment of low vapor pressure deficit in the upper boundary layer which helps maintain pollen viability.

Development and Implementation of Dynamic Scripts to Support Local Model Verification at National Weather Service Weather Forecast Offices

NASA Technical Reports Server (NTRS)

Zavordsky, Bradley; Case, Jonathan L.; Gotway, John H.; White, Kristopher; Medlin, Jeffrey; Wood, Lance; Radell, Dave

2014-01-01

Local modeling with a customized configuration is conducted at National Weather Service (NWS) Weather Forecast Offices (WFOs) to produce high-resolution numerical forecasts that can better simulate local weather phenomena and complement larger scale global and regional models. The advent of the Environmental Modeling System (EMS), which provides a pre-compiled version of the Weather Research and Forecasting (WRF) model and wrapper Perl scripts, has enabled forecasters to easily configure and execute the WRF model on local workstations. NWS WFOs often use EMS output to help in forecasting highly localized, mesoscale features such as convective initiation, the timing and inland extent of lake effect snow bands, lake and sea breezes, and topographically-modified winds. However, quantitatively evaluating model performance to determine errors and biases still proves to be one of the challenges in running a local model. Developed at the National Center for Atmospheric Research (NCAR), the Model Evaluation Tools (MET) verification software makes performing these types of quantitative analyses easier, but operational forecasters do not generally have time to familiarize themselves with navigating the sometimes complex configurations associated with the MET tools. To assist forecasters in running a subset of MET programs and capabilities, the Short-term Prediction Research and Transition (SPoRT) Center has developed and transitioned a set of dynamic, easily configurable Perl scripts to collaborating NWS WFOs. The objective of these scripts is to provide SPoRT collaborating partners in the NWS with the ability to evaluate the skill of their local EMS model runs in near real time with little prior knowledge of the MET package. The ultimate goal is to make these verification scripts available to the broader NWS community in a future version of the EMS software. This paper provides an overview of the SPoRT MET scripts, instructions for how the scripts are run, and example use cases.

Recent Advances in WRF Modeling for Air Quality Applications

EPA Science Inventory

The USEPA uses WRF in conjunction with the Community Multiscale Air Quality (CMAQ) for air quality regulation and research. Over the years we have added physics options and geophysical datasets to the WRF system to enhance model capabilities especially for extended retrospective...

Assessing regional scale predictions of aerosols, marine stratocumulus, and their interactions during VOCALS-REx using WRF-Chem

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Qing; Gustafson, William I.; Fast, Jerome D.

2011-12-02

In the recent chemistry version (v3.3) of the Weather Research and Forecasting (WRF-Chem) model, we have coupled the Morrison double-moment microphysics scheme with interactive aerosols so that full two-way aerosol-cloud interactions are included in simulations. We have used this new WRF-Chem functionality in a study focused on assessing predictions of aerosols, marine stratocumulus clouds, and their interactions over the Southeast Pacific using measurements from the VAMOS Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-REx) and satellite retrievals. This study also serves as a detailed analysis of our WRF-Chem simulations contributed to the VOCALS model Assessment (VOCA) project. The WRF-Chem 31-day (October 15-November 16,more » 2008) simulation with aerosol-cloud interactions (AERO hereafter) is also compared to a simulation (MET hereafter) with fixed cloud droplet number concentrations assumed by the default in Morrison microphysics scheme with no interactive aerosols. The well-predicted aerosol properties such as number, mass composition, and optical depth lead to significant improvements in many features of the predicted stratocumulus clouds: cloud optical properties and microphysical properties such as cloud top effective radius, cloud water path, and cloud optical thickness, and cloud macrostructure such as cloud depth and cloud base height. These improvements in addition to the aerosol direct and semi-direct effects, in turn, feed back to the prediction of boundary-layer characteristics and energy budgets. Particularly, inclusion of interactive aerosols in AERO strengths temperature and humidity gradients within capping inversion layer and lowers the MBL depth by 150 m from that of the MET simulation. Mean top-of-the-atmosphere outgoing shortwave fluxes, surface latent heat, and surface downwelling longwave fluxes are in better agreement with observations in AERO, compared to the MET simulation. Nevertheless, biases in some of the simulated meteorological quantities (e.g., MBL temperature and humidity over the remote ocean) and aerosol quantities (e.g., overestimations of supermicron sea salt mass) might affect simulated stratocumulus and energy fluxes over the SEP, and require further investigations. Although not perfect, the overall performance of the regional model in simulating mesoscale aerosol-cloud interactions is encouraging and suggests that the inclusion of spatially varying aerosol characteristics is important when simulating marine stratocumulus over the southeastern Pacific.« less

Coupling of WRF and Building-resolved CFD Simulations for Greenhouse Gas Transport and Dispersion

NASA Astrophysics Data System (ADS)

Prasad, K.; Hu, H.; McDermott, R.; Lopez-Coto, I.; Davis, K. J.; Whetstone, J. R.; Lauvaux, T.

2014-12-01

The Indianapolis Flux Experiment (INFLUX) aims to use a top-down inversion methodology to quantify sources of Greenhouse Gas (GHG) emissions over an urban domain with high spatial and temporal resolution. Atmospheric transport of tracer gases from an emission source to a tower mounted receptor are usually conducted using the Weather Research and Forecasting (WRF) model. WRF is used extensively in the atmospheric community to simulate mesoscale atmospheric transport. For such simulations, WRF employs a parameterized turbulence model and does not resolve the fine scale dynamics that are generated by the flow around buildings and communities that are part of a large city. Since the model domain includes the city of Indianapolis, much of the flow of interest is over an urban topography. The NIST Fire Dynamics Simulator (FDS) is a computational fluid dynamics model to perform large eddy simulations of flow around buildings, but it has not been nested within a larger-scale atmospheric transport model such as WRF. FDS has the potential to evaluate the impact of complex urban topography on near-field dispersion and mixing that cannot be simulated with a mesoscale atmospheric model, and which may be important to determining urban GHG emissions using atmospheric measurements. A methodology has been developed to run FDS as a sub-grid scale model within a WRF simulation. The coupling is based on nudging the FDS flow field towards the one computed by WRF, and is currently limited to one way coupling performed in an off-line mode. Using the coupled WRF / FDS model, NIST will investigate the effects of the urban canopy at horizontal resolutions of 2-10 m. The coupled WRF-FDS simulations will be used to calculate the dispersion of tracer gases in an urban domain and to evaluate the upwind areas that contribute to tower observations, referred to in the inversion community as influence functions. Predicted mixing ratios will be compared with tower measurements and WRF simulations, and FDS influence functions will be compared with those generated from WRF and the Lagrangian Particle Dispersion Model. Results of this study will provide guidance regarding the importance of explicit simulations of urban atmospheric turbulence in obtaining accurate estimates of greenhouse gas emissions.

WRF-Chem simulations in the Amazon region during wet and dry season transitions: evaluation of methane models and wetland inundation maps

NASA Astrophysics Data System (ADS)

Beck, V.; Gerbig, C.; Koch, T.; Bela, M. M.; Longo, K. M.; Freitas, S. R.; Kaplan, J. O.; Prigent, C.; Bergamaschi, P.; Heimann, M.

2012-09-01

The Amazon region as a large source of methane (CH4) contributes significantly to the global annual CH4 budget. For the first time in the Amazon region, a forward and inverse modelling framework on regional scale for the purpose of assessing the CH4 budget of the Amazon region is implemented. Here, we present forward simulations of CH4 based on a modified version of the Weather Research and Forecasting model with chemistry that allows for passive tracer transport of CH4, carbon monoxide, and carbon dioxide (WRF-GHG), in combination with two different process-based bottom-up models of CH4 emissions from anaerobic microbial production in wetlands and additional datasets prescribing CH4 emissions from other sources such as biomass burning, termites, or other anthropogenic emissions. We compare WRF-GHG simulations on 10 km horizontal resolution to flask and continuous CH4 observations obtained during two airborne measurement campaigns within the Balanço Atmosférico Regional de Carbono na Amazônia (BARCA) project in November 2008 and May 2009. In addition, three different wetland inundation maps, prescribing the fraction of inundated area per grid cell, are evaluated. Our results indicate that the wetland inundation map with inundated area changing in time represents the observations best except for the northern part of the Amazon basin and the Manaus area. WRF-GHG was able to represent the observed CH4 mixing ratios best at days with less convective activity. After adjusting wetland emissions to match the averaged observed mixing ratios of flights with little convective activity, the monthly CH4 budget of the Amazon lowland region obtained from four different simulations ranges from 1.5 to 4.8 Tg for November 2008 and from 1.3 to 5.5 Tg for May 2009. This corresponds to an average CH4 flux of 9-31 mg m-2 d-1 for November 2008 and 8-36 mg m-2 d-1 for May 2009.

Development of extended WRF variational data assimilation system (WRFDA) for WRF non-hydrostatic mesoscale model

NASA Astrophysics Data System (ADS)

Pattanayak, Sujata; Mohanty, U. C.

2018-06-01

The paper intends to present the development of the extended weather research forecasting data assimilation (WRFDA) system in the framework of the non-hydrostatic mesoscale model core of weather research forecasting system (WRF-NMM), as an imperative aspect of numerical modeling studies. Though originally the WRFDA provides improved initial conditions for advanced research WRF, we have successfully developed a unified WRFDA utility that can be used by the WRF-NMM core, as well. After critical evaluation, it has been strategized to develop a code to merge WRFDA framework and WRF-NMM output. In this paper, we have provided a few selected implementations and initial results through single observation test, and background error statistics like eigenvalues, eigenvector and length scale among others, which showcase the successful development of extended WRFDA code for WRF-NMM model. Furthermore, the extended WRFDA system is applied for the forecast of three severe cyclonic storms: Nargis (27 April-3 May 2008), Aila (23-26 May 2009) and Jal (4-8 November 2010) formed over the Bay of Bengal. Model results are compared and contrasted within the analysis fields and later on with high-resolution model forecasts. The mean initial position error is reduced by 33% with WRFDA as compared to GFS analysis. The vector displacement errors in track forecast are reduced by 33, 31, 30 and 20% to 24, 48, 72 and 96 hr forecasts respectively, in data assimilation experiments as compared to control run. The model diagnostics indicates successful implementation of WRFDA within the WRF-NMM system.

Simulating Roll Clouds associated with Low-Level Convergence.

NASA Astrophysics Data System (ADS)

Prasad, A. A.; Sherwood, S. C.

2015-12-01

Convective initiation often takes place when features such as fronts and/or rolls collide, merge or otherwise meet. Rolls indicate boundary layer convergence and may initiate thunderstorms. These are often seen in satellite and radar imagery prior to the onset of deep convection. However, links between convergence driven rolls and convection are poor in global models. The poor representation of convection is the source of many model biases, especially over the Maritime Continent in the Tropics. We simulate low-level convergence lines over north-eastern Australia using the Weather Research and Forecasting (WRF) Model (version 3.7). The simulations are events from September-October 2002 driven by sea breeze circulations. Cloud lines associated with bore-waves that form along the low-level convergence lines are thoroughly investigated in this study with comparisons from satellite and surface observations. Initial simulations for a series of cloud lines observed on 4th October, 2002 over the Gulf of Carpentaria showed greater agreement in the timing and propagation of the disturbance and the low-level convergence, however the cloud lines or streets of roll clouds were not properly captured by the model. Results from a number of WRF simulations with different microphysics, cumulus and planetary boundary layer schemes, resolution and boundary conditions will also be discussed.

Potential Technologies for Assessing Risk Associated with a Mesoscale Forecast

DTIC Science & Technology

2015-10-01

American GFS models, and informally applied on the Weather Research and Forecasting ( WRF ) model. The current CI equation is as follows...Reen B, Penc R. Investigating surface bias errors in the Weather Research and Forecasting ( WRF ) model using a Geographic Information System (GIS). J...Forecast model ( WRF -ARW) with extensions that might include finer terrain resolutions and more detailed representations of the underlying atmospheric

Configuring the HYSPLIT Model for National Weather Service Forecast Office and Spaceflight Meteorology Group Applications

NASA Technical Reports Server (NTRS)

Dreher, Joseph G.

2009-01-01

For expedience in delivering dispersion guidance in the diversity of operational situations, National Weather Service Melbourne (MLB) and Spaceflight Meteorology Group (SMG) are becoming increasingly reliant on the PC-based version of the HYSPLIT model run through a graphical user interface (GUI). While the GUI offers unique advantages when compared to traditional methods, it is difficult for forecasters to run and manage in an operational environment. To alleviate the difficulty in providing scheduled real-time trajectory and concentration guidance, the Applied Meteorology Unit (AMU) configured a Linux version of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) (HYSPLIT) model that ingests the National Centers for Environmental Prediction (NCEP) guidance, such as the North American Mesoscale (NAM) and the Rapid Update Cycle (RUC) models. The AMU configured the HYSPLIT system to automatically download the NCEP model products, convert the meteorological grids into HYSPLIT binary format, run the model from several pre-selected latitude/longitude sites, and post-process the data to create output graphics. In addition, the AMU configured several software programs to convert local Weather Research and Forecast (WRF) model output into HYSPLIT format.

Air Quality Modeling and Forecasting over the United States Using WRF-Chem

NASA Astrophysics Data System (ADS)

Boxe, C.; Hafsa, U.; Blue, S.; Emmanuel, S.; Griffith, E.; Moore, J.; Tam, J.; Khan, I.; Cai, Z.; Bocolod, B.; Zhao, J.; Ahsan, S.; Gurung, D.; Tang, N.; Bartholomew, J.; Rafi, R.; Caltenco, K.; Rivas, M.; Ditta, H.; Alawlaqi, H.; Rowley, N.; Khatim, F.; Ketema, N.; Strothers, J.; Diallo, I.; Owens, C.; Radosavljevic, J.; Austin, S. A.; Johnson, L. P.; Zavala-Gutierrez, R.; Breary, N.; Saint-Hilaire, D.; Skeete, D.; Stock, J.; Salako, O.

2016-12-01

WRF-Chem is the Weather Research and Forecasting (WRF) model coupled with Chemistry. The model simulates the emission, transport, mixing, and chemical transformation of trace gases and aerosols simultaneously with the meteorology. The model is used for investigation of regional-scale air quality, field program analysis, and cloud-scale interactions between clouds and chemistry. The development of WRF-Chem is a collaborative effort among the community led by NOAA/ESRL scientists. The Official WRF-Chem web page is located at the NOAA web site. Our model development is closely linked with both NOAA/ESRL and DOE/PNNL efforts. Description of PNNL WRF-Chem model development is located at the PNNL web site as well as the PNNL Aerosol Modeling Testbed. High school and undergraduate students, representative of academic institutions throughout USA's Tri-State Area (New York, New Jersey, Connecticut), set up WRF-Chem on CUNY CSI's High Performance Computing Center. Students learned the back-end coding that governs WRF-Chems structure and the front-end coding that displays visually specified weather simulations and forecasts. Students also investigated the impact, to select baseline simulations/forecasts, due to the reaction, NO2 + OH + M → HOONO + M (k = 9.2 × 10-12 cm3 molecule-1 s-1, Mollner et al. 2010). The reaction of OH and NO2 to form gaseous nitric acid (HONO2) is among the most influential and in atmospheric chemistry. Till a few years prior, its rate coefficient remained poorly determined under tropospheric conditions because of difficulties in making laboratory measurements at 760 torr. These activities fosters student coding competencies and deep insights into weather forecast and air quality.

WRF-Hydro Simulated Spatiotemporal Characteristics of Streamflow Extremes over the CONUS during 1993-2016 and Possible Connections with Climate Variability

NASA Astrophysics Data System (ADS)

Dugger, A. L.; Zhang, Y.; Gochis, D.; Yu, W.; McCreight, J. L.; Karsten, L.; Rafieeinasab, A.; Sampson, K. M.; Salas, F.; Read, L.; Pan, L.; Yates, D. N.; Cosgrove, B.; Clark, E. P.

2017-12-01

Streamflow extremes (lows and peaks) tend to have disproportionately higher impacts on the human and natural systems compared to mean streamflow. Examining and understanding the spatiotemporal distributions of streamflow extremes is of significant interests to both the research community and the water resources management. In this work, the output from the 24-year (1993 through 2016) retrospective runs of the National Water Model (NWM) version of WRF-Hydro will be analyzed for streamflow extremes over the CONUS domain. The CONUS domain was configured at 1-km resolution for land surface grid and 250-m resolution for terrain routing. The WRF-Hydro runs were forced by the regridded and downscaled NLDAS2 data. The analyses focus on daily mean streamflow values over the full water year and within the summer and winter seasons. Connections between NWM streamflow and other hydrologic variables (e.g. snowpack, soil moisture/saturation and ET) with variations in large-scale climate phenomena, e.g., El Niño - Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), and North American monsoon are examined. The CONUS domain has a diverse environment and is characterized by complex terrain, heterogeneous land surfaces and ecosystems, and numerous hydrological basins. The potential dependence of streamflow extremes on regional terrain character, climatic conditions, and ecologic zones will also be investigated.

Adding Four- Dimensional Data Assimilation (aka grid ...

EPA Pesticide Factsheets

Adding four-dimensional data assimilation (a.k.a. grid nudging) to MPAS.The U.S. Environmental Protection Agency is investigating the use of MPAS as the meteorological driver for its next-generation air quality model. To function as such, MPAS needs to operate in a diagnostic mode in much the same manner as the current meteorological driver, the Weather Research and Forecasting (WRF) model. The WRF operates in diagnostic mode using Four-Dimensional Data Assimilation, also known as "grid nudging". MPAS version 4.0 has been modified with the addition of an FDDA routine to the standard physics drivers to nudge the state variables for wind, temperature and water vapor towards MPAS initialization fields defined at 6-hour intervals from GFS-derived data. The results to be shown demonstrate the ability to constrain MPAS simulations to known historical conditions and thus provide the U.S. EPA with a practical meteorological driver for global-scale air quality simulations. The National Exposure Research Laboratory (NERL) Computational Exposure Division (CED) develops and evaluates data, decision-support tools, and models to be applied to media-specific or receptor-specific problem areas. CED uses modeling-based approaches to characterize exposures, evaluate fate and transport, and support environmental diagnostics/forensics with input from multiple data sources. It also develops media- and receptor-specific models, process models, and decision support tools for use bo

A high resolution WRF model for wind energy forecasting

NASA Astrophysics Data System (ADS)

Vincent, Claire Louise; Liu, Yubao

2010-05-01

The increasing penetration of wind energy into national electricity markets has increased the demand for accurate surface layer wind forecasts. There has recently been a focus on forecasting the wind at wind farm sites using both statistical models and numerical weather prediction (NWP) models. Recent advances in computing capacity and non-hydrostatic NWP models means that it is possible to nest mesoscale models down to Large Eddy Simulation (LES) scales over the spatial area of a typical wind farm. For example, the WRF model (Skamarock 2008) has been run at a resolution of 123 m over a wind farm site in complex terrain in Colorado (Liu et al. 2009). Although these modelling attempts indicate a great hope for applying such models for detailed wind forecasts over wind farms, one of the obvious challenges of running the model at this resolution is that while some boundary layer structures are expected to be modelled explicitly, boundary layer eddies into the inertial sub-range can only be partly captured. Therefore, the amount and nature of sub-grid-scale mixing that is required is uncertain. Analysis of Liu et al. (2009) modelling results in comparison to wind farm observations indicates that unrealistic wind speed fluctuations with a period of around 1 hour occasionally occurred during the two day modelling period. The problem was addressed by re-running the same modelling system with a) a modified diffusion constant and b) two-way nesting between the high resolution model and its parent domain. The model, which was run with horizontal grid spacing of 370 m, had dimensions of 505 grid points in the east-west direction and 490 points in the north-south direction. It received boundary conditions from a mesoscale model of resolution 1111 m. Both models had 37 levels in the vertical. The mesoscale model was run with a non-local-mixing planetary boundary layer scheme, while the 370 m model was run with no planetary boundary layer scheme. It was found that increasing the diffusion constant caused damping of the unrealistic fluctuations, but did not completely solve the problem. Using two-way nesting also mitigated the unrealistic fluctuations significantly. It can be concluded that for real case LES modelling of wind farm circulations, care should be taken to ensure the consistency between the mesoscale weather forcing and LES models to avoid exciting spurious noise along the forcing boundary. The development of algorithms that adequately model the sub-grid-scale mixing that cannot be resolved by LES models is an important area for further research. References Liu, Y. Y._W. Liu, W. Y.Y. Cheng, W. Wu, T. T. Warner and K. Parks, 2009: Simulating intra-farm wind variations with the WRF-RTFDDA-LES modeling system. 10th WRF Users' Workshop, Boulder, C, USA. June 23 - 26, 2009. Skamarock, W., J. Dudhia, D.O. Gill, D.M. Barker, M.G.Duda, X-Y. Huang, W. Wang and J.G. Powers, A Description of the Advanced Research WRF version 3, NCAR Technical Note TN-475+STR, NCAR, Boulder, Colorado, 2008.

The Impact of Incongruous Lake Temperatures on Regional Climate Extremes Downscaled from the CMIP5 Archive Using the WRF Model

EPA Science Inventory

The impact of incongruous lake temperatures is demonstrated using the Weather Research and Forecasting (WRF) Model to downscale global climate fields. Unrealistic lake temperatures prescribed by the default WRF configuration cause obvious biases near the lakes and also affect pre...

Bias Correction for Assimilation of Retrieved AIRS Profiles of Temperature and Humidity

NASA Technical Reports Server (NTRS)

Blankenship, Clay; Zavodsky, Brad; Blackwell, William

2014-01-01

Atmospheric Infrared Sounder (AIRS) is a hyperspectral radiometer aboard NASA's Aqua satellite designed to measure atmospheric profiles of temperature and humidity. AIRS retrievals are assimilated into the Weather Research and Forecasting (WRF) model over the North Pacific for some cases involving "atmospheric rivers". These events bring a large flux of water vapor to the west coast of North America and often lead to extreme precipitation in the coastal mountain ranges. An advantage of assimilating retrievals rather than radiances is that information in partly cloudy fields of view can be used. Two different Level 2 AIRS retrieval products are compared: the Version 6 AIRS Science Team standard retrievals and a neural net retrieval from MIT. Before assimilation, a bias correction is applied to adjust each layer of retrieved temperature and humidity so the layer mean values agree with a short-term model climatology. WRF runs assimilating each of the products are compared against each other and against a control run with no assimilation. This paper will describe the bias correction technique and results from forecasts evaluated by validation against a Total Precipitable Water (TPW) product from CIRA and against Global Forecast System (GFS) analyses.

A Climate Statistics Tool and Data Repository

NASA Astrophysics Data System (ADS)

Wang, J.; Kotamarthi, V. R.; Kuiper, J. A.; Orr, A.

2017-12-01

Researchers at Argonne National Laboratory and collaborating organizations have generated regional scale, dynamically downscaled climate model output using Weather Research and Forecasting (WRF) version 3.3.1 at a 12km horizontal spatial resolution over much of North America. The WRF model is driven by boundary conditions obtained from three independent global scale climate models and two different future greenhouse gas emission scenarios, named representative concentration pathways (RCPs). The repository of results has a temporal resolution of three hours for all the simulations, includes more than 50 variables, is stored in Network Common Data Form (NetCDF) files, and the data volume is nearly 600Tb. A condensed 800Gb set of NetCDF files were made for selected variables most useful for climate-related planning, including daily precipitation, relative humidity, solar radiation, maximum temperature, minimum temperature, and wind. The WRF model simulations are conducted for three 10-year time periods (1995-2004, 2045-2054, and 2085-2094), and two future scenarios RCP4.5 and RCP8.5). An open-source tool was coded using Python 2.7.8 and ESRI ArcGIS 10.3.1 programming libraries to parse the NetCDF files, compute summary statistics, and output results as GIS layers. Eight sets of summary statistics were generated as examples for the contiguous U.S. states and much of Alaska, including number of days over 90°F, number of days with a heat index over 90°F, heat waves, monthly and annual precipitation, drought, extreme precipitation, multi-model averages, and model bias. This paper will provide an overview of the project to generate the main and condensed data repositories, describe the Python tool and how to use it, present the GIS results of the computed examples, and discuss some of the ways they can be used for planning. The condensed climate data, Python tool, computed GIS results, and documentation of the work are shared on the Internet.

Atmospheric Profiles, Clouds, and the Evolution of Sea Ice Cover in the Beaufort and Chukchi Seas: Atmospheric Observations and Modeling as Part of the SeasonalIce Zone Reconnaissance Surveys

DTIC Science & Technology

2015-09-30

hired to conduct WRF model experiments. • We conducted Weather Research and Forecast ( WRF ) model simulations for the summer of 2014 and compared with... WRF simulations under different synoptic conditions will help to more 10 clearly identify the deficiencies in the representation of these processes

Evaluation of the two-way coupled WRF-CMAQ modeling system to the 2011 DISCOVER-AQ campaign at 12-km, 4-km and 1-km resolutions

EPA Science Inventory

At the 12th Annual CMAS Conference initial results from the application of the coupled WRF-CMAQ modeling system to the 2011 Baltimore-Washington D.C. DISCOVER-AQ campaign were presented, with the focus on updates and new methods applied to the WRF modeling for fine-scale applicat...

WRF and WRF-Chem v3.5.1 simulations of meteorology and black carbon concentrations in the Kathmandu Valley

NASA Astrophysics Data System (ADS)

Mues, Andrea; Lauer, Axel; Lupascu, Aurelia; Rupakheti, Maheswar; Kuik, Friderike; Lawrence, Mark G.

2018-06-01

An evaluation of the meteorology simulated using the Weather Research and Forecast (WRF) model for the region of south Asia and Nepal with a focus on the Kathmandu Valley is presented. A particular focus of the model evaluation is placed on meteorological parameters that are highly relevant to air quality such as wind speed and direction, boundary layer height and precipitation. The same model setup is then used for simulations with WRF including chemistry and aerosols (WRF-Chem). A WRF-Chem simulation has been performed using the state-of-the-art emission database, EDGAR HTAP v2.2, which is the Emission Database for Global Atmospheric Research of the Joint Research Centre (JRC) of the European Commission, in cooperation with the Task Force on Hemispheric Transport of Air Pollution (TF HTAP) organized by the United Nations Economic Commission for Europe, along with a sensitivity simulation using observation-based black carbon emission fluxes for the Kathmandu Valley. The WRF-Chem simulations are analyzed in comparison to black carbon measurements in the valley and to each other. The evaluation of the WRF simulation with a horizontal resolution of 3×3 km2 shows that the model is often able to capture important meteorological parameters inside the Kathmandu Valley and the results for most meteorological parameters are well within the range of biases found in other WRF studies especially in mountain areas. But the evaluation results also clearly highlight the difficulties of capturing meteorological parameters in such complex terrain and reproducing subgrid-scale processes with a horizontal resolution of 3×3 km2. The measured black carbon concentrations are typically systematically and strongly underestimated by WRF-Chem. A sensitivity study with improved emissions in the Kathmandu Valley shows significantly reduced biases but also underlines several limitations of such corrections. Further improvements of the model and of the emission data are needed before being able to use the model to robustly assess air pollution mitigation scenarios in the Kathmandu region.

Impacts of Typhoon Megi (2010) on the South China Sea

DTIC Science & Technology

2014-06-01

investigations. To obtain realistic typhoon-strength atmospheric forcing, the EASNFS applied typhoon-resolving Weather Research and Forecasting ( WRF ) model wind...EASNFS applied typhoon-resolving Weather Research and Forecasting ( WRF ) model wind field blended with global weather forecast winds from the U.S. Navy...only 1C. Sequential SST snapshots, of which only a Figure 1. The EASNFS model domain with topography and an inset covered by WRF model. Typhoon Megi’s

Developing Snow Model Forcing Data From WRF Model Output to Aid in Water Resource Forecasting

NASA Astrophysics Data System (ADS)

Havens, S.; Marks, D. G.; Watson, K. A.; Masarik, M.; Flores, A. N.; Kormos, P.; Hedrick, A. R.

2015-12-01

Traditional operational modeling tools used by water managers in the west are challenged by more frequently occurring uncharacteristic stream flow patterns caused by climate change. Water managers are now turning to new models based on the physical processes within a watershed to combat the increasing number of events that do not follow the historical patterns. The USDA-ARS has provided near real time snow water equivalent (SWE) maps using iSnobal since WY2012 for the Boise River Basin in southwest Idaho and since WY2013 for the Tuolumne Basin in California that feeds the Hetch Hetchy reservoir. The goal of these projects is to not only provide current snowpack estimates but to use the Weather Research and Forecasting (WRF) model to drive iSnobal in order to produce a forecasted stream flow when coupled to a hydrology model. The first step is to develop methods on how to create snow model forcing data from WRF outputs. Using a reanalysis 1km WRF dataset from WY2009 over the Boise River Basin, WRF model results like surface air temperature, relative humidity, wind, precipitation, cloud cover, and incoming long wave radiation must be downscaled for use in iSnobal. iSnobal results forced with WRF output are validated at point locations throughout the basin, as well as compared with iSnobal results forced with traditional weather station data. The presentation will explore the differences in forcing data derived from WRF outputs and weather stations and how this affects the snowpack distribution.

Weather Research and Forecasting Model Sensitivity Comparisons for Warm Season Convective Initiation

NASA Technical Reports Server (NTRS)

Watson, Leela R.

2007-01-01

This report describes the work done by the Applied Meteorology Unit (AMU) in assessing the success of different model configurations in predicting warm season convection over East-Central Florida. The Weather Research and Forecasting Environmental Modeling System (WRF EMS) software allows users to choose among two dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM). There are also data assimilation analysis packages available for the initialization of the WRF model - the Local Analysis and Prediction System (LAPS) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS). Besides model core and initialization options, the WRF model can be run with one- or two-way nesting. Having a series of initialization options and WRF cores, as well as many options within each core, creates challenges for local forecasters, such as determining which configuration options are best to address specific forecast concerns. This project assessed three different model intializations available to determine which configuration best predicts warm season convective initiation in East-Central Florida. The project also examined the use of one- and two-way nesting in predicting warm season convection.

The Sensitivity of WRF Daily Summertime Simulations over West Africa to Alternative Parameterizations. Part 1: African Wave Circulation

NASA Technical Reports Server (NTRS)

Noble, Erik; Druyan, Leonard M.; Fulakeza, Matthew

2014-01-01

The performance of the NCAR Weather Research and Forecasting Model (WRF) as a West African regional-atmospheric model is evaluated. The study tests the sensitivity of WRF-simulated vorticity maxima associated with African easterly waves to 64 combinations of alternative parameterizations in a series of simulations in September. In all, 104 simulations of 12-day duration during 11 consecutive years are examined. The 64 combinations combine WRF parameterizations of cumulus convection, radiation transfer, surface hydrology, and PBL physics. Simulated daily and mean circulation results are validated against NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) and NCEP/Department of Energy Global Reanalysis 2. Precipitation is considered in a second part of this two-part paper. A wide range of 700-hPa vorticity validation scores demonstrates the influence of alternative parameterizations. The best WRF performers achieve correlations against reanalysis of 0.40-0.60 and realistic amplitudes of spatiotemporal variability for the 2006 focus year while a parallel-benchmark simulation by the NASA Regional Model-3 (RM3) achieves higher correlations, but less realistic spatiotemporal variability. The largest favorable impact on WRF-vorticity validation is achieved by selecting the Grell-Devenyi cumulus convection scheme, resulting in higher correlations against reanalysis than simulations using the Kain-Fritch convection. Other parameterizations have less-obvious impact, although WRF configurations incorporating one surface model and PBL scheme consistently performed poorly. A comparison of reanalysis circulation against two NASA radiosonde stations confirms that both reanalyses represent observations well enough to validate the WRF results. Validation statistics for optimized WRF configurations simulating the parallel period during 10 additional years are less favorable than for 2006.

Simulation of an Extreme Off Season Rainy Event over Senegal Using WRF ARW Model: A focus on dynamic, thermodynamic processes and predictability

NASA Astrophysics Data System (ADS)

Sarr, A.

2016-12-01

This study investigates less known weather events, Off Season Rain affecting during boreal winter Western parts of Sahel region mainly, Senegal, Cape Verde and Mauritania. They are characterized by cloudy conditions at mid level, which can trigger light long lasting rains. In January 2002, an extreme case occurred from 09 to 11th producing unusual heavy rains, which had dramatic consequences on livestock and irrigated crops. The Weather and Research Forecast model (WRF ARW version 3.4) is used to simulate the event, which affected the western coast around the land/ocean interface and caused huge damages in Senegal and Mauritania. The model was able to reasonably simulate the event and its intensity 2 to 3 days in advance, demonstrating the usefulness of such a tools for early warning system (EWS), which could help mitigate the impacts. The location of the rain band was closer to the observed situation in higher resolution domains. The study showed keys dynamic and thermodynamic conditions associated with the event. Precipitable water (PW) evolution played a central role on the intensity of the event. The deep trough, associated with the disturbance, forced a northeast transport of moisture from the Inter Tropical Convergence Zone (ITCZ) over the Ocean towards Senegal and Mauritania.

WRF4SG: A Scientific Gateway for climate experiment workflows

NASA Astrophysics Data System (ADS)

Blanco, Carlos; Cofino, Antonio S.; Fernandez-Quiruelas, Valvanuz

2013-04-01

The Weather Research and Forecasting model (WRF) is a community-driven and public domain model widely used by the weather and climate communities. As opposite to other application-oriented models, WRF provides a flexible and computationally-efficient framework which allows solving a variety of problems for different time-scales, from weather forecast to climate change projection. Furthermore, WRF is also widely used as a research tool in modeling physics, dynamics, and data assimilation by the research community. Climate experiment workflows based on Weather Research and Forecasting (WRF) are nowadays among the one of the most cutting-edge applications. These workflows are complex due to both large storage and the huge number of simulations executed. In order to manage that, we have developed a scientific gateway (SG) called WRF for Scientific Gateway (WRF4SG) based on WS-PGRADE/gUSE and WRF4G frameworks to ease achieve WRF users needs (see [1] and [2]). WRF4SG provides services for different use cases that describe the different interactions between WRF users and the WRF4SG interface in order to show how to run a climate experiment. As WS-PGRADE/gUSE uses portlets (see [1]) to interact with users, its portlets will support these use cases. A typical experiment to be carried on by a WRF user will consist on a high-resolution regional re-forecast. These re-forecasts are common experiments used as input data form wind power energy and natural hazards (wind and precipitation fields). In the cases below, the user is able to access to different resources such as Grid due to the fact that WRF needs a huge amount of computing resources in order to generate useful simulations: * Resource configuration and user authentication: The first step is to authenticate on users' Grid resources by virtual organizations. After login, the user is able to select which virtual organization is going to be used by the experiment. * Data assimilation: In order to assimilate the data sources, the user has to select them browsing through LFC Portlet. * Design Experiment workflow: In order to configure the experiment, the user will define the type of experiment (i.e. re-forecast), and its attributes to simulate. In this case the main attributes are: the field of interest (wind, precipitation, ...), the start and end date simulation and the requirements of the experiment. * Monitor workflow: In order to monitor the experiment the user will receive notification messages based on events and also the gateway will display the progress of the experiment. * Data storage: Like Data assimilation case, the user is able to browse and view the output data simulations using LFC Portlet. The objectives of WRF4SG can be described by considering two goals. The first goal is to show how WRF4SG facilitates to execute, monitor and manage climate workflows based on the WRF4G framework. And the second goal of WRF4SG is to help WRF users to execute their experiment workflows concurrently using heterogeneous computing resources such as HPC and Grid. [1] Kacsuk, P.: P-GRADE portal family for grid infrastructures. Concurrency and Computation: Practice and Experience. 23, 235-245 (2011). [2] http://www.meteo.unican.es/software/wrf4g

Atmospheric Profiles, Clouds, and the Evolution of Sea Ice Cover in the Beaufort and Chukchi Seas: Atmospheric Observations and Modeling as Part of the Seasonal Ice Zone Reconnaissance Surveys

DTIC Science & Technology

2015-09-30

to conduct WRF model experiments.  We conducted Weather Research and Forecast ( WRF ) model simulations for the summer of 2014 and compared with the...level winds might be more important forcing for sea ice. In addition, evaluation of Polar- WRF simulations under different synoptic conditions will help

Effect of different emission inventories on modeled ozone and carbon monoxide in Southeast Asia

NASA Astrophysics Data System (ADS)

Amnuaylojaroen, T.; Barth, M. C.; Emmons, L. K.; Carmichael, G. R.; Kreasuwun, J.; Prasitwattanaseree, S.; Chantara, S.

2014-12-01

In order to improve our understanding of air quality in Southeast Asia, the anthropogenic emissions inventory must be well represented. In this work, we apply different anthropogenic emission inventories in the Weather Research and Forecasting Model with Chemistry (WRF-Chem) version 3.3 using Model for Ozone and Related Chemical Tracers (MOZART) gas-phase chemistry and Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) aerosols to examine the differences in predicted carbon monoxide (CO) and ozone (O3) surface mixing ratios for Southeast Asia in March and December 2008. The anthropogenic emission inventories include the Reanalysis of the TROpospheric chemical composition (RETRO), the Intercontinental Chemical Transport Experiment-Phase B (INTEX-B), the MACCity emissions (adapted from the Monitoring Atmospheric Composition and Climate and megacity Zoom for the Environment projects), the Southeast Asia Composition, Cloud, Climate Coupling Regional Study (SEAC4RS) emissions, and a combination of MACCity and SEAC4RS emissions. Biomass-burning emissions are from the Fire Inventory from the National Center for Atmospheric Research (NCAR) (FINNv1) model. WRF-Chem reasonably predicts the 2 m temperature, 10 m wind, and precipitation. In general, surface CO is underpredicted by WRF-Chem while surface O3 is overpredicted. The NO2 tropospheric column predicted by WRF-Chem has the same magnitude as observations, but tends to underpredict the NO2 column over the equatorial ocean and near Indonesia. Simulations using different anthropogenic emissions produce only a slight variability of O3 and CO mixing ratios, while biomass-burning emissions add more variability. The different anthropogenic emissions differ by up to 30% in CO emissions, but O3 and CO mixing ratios averaged over the land areas of the model domain differ by ~4.5% and ~8%, respectively, among the simulations. Biomass-burning emissions create a substantial increase for both O3 and CO by ~29% and ~16%, respectively, when comparing the March biomass-burning period to the December period with low biomass-burning emissions. The simulations show that none of the anthropogenic emission inventories are better than the others for predicting O3 surface mixing ratios. However, the simulations with different anthropogenic emission inventories do differ in their predictions of CO surface mixing ratios producing variations of ~30% for March and 10-20% for December at Thai surface monitoring sites.

Charactering biomass burning aerosol in the Weather Research and Forecasting model with Chemistry (WRF-Chem), with evaluation against SAMBBA flight data.

NASA Astrophysics Data System (ADS)

Archer-Nicholls, S.; Lowe, D.; Darbyshire, E.; Morgan, W.; Freitas, S. R.; Longo, K.; Coe, H.; McFiggans, G.

2014-12-01

The burning of forests in the Amazonia region is a globally significant source of carbonaceous aerosol, containing both absorbing and scattering components. Biomass burning aerosol (BBA) are efficient CCN, modifying cloud properties and influencing atmospheric circulation and precipitation tendencies. The impacts of BBA are highly dependent on their size distribution and composition. Studies in this region can therefore benefit greatly from the use of state-of-the-art sectional aerosol representations. A bottom-up fire emissions inventory, 3BEM, has been developed by Longo et al.1. It uses satellite products to identify fire locations, applying the emissions factors of Andrei and Merlot3 to generate daily emission maps. Flaming emissions are very buoyant, and a method for injecting emissions at altitude is needed to accurately describe the vertical profile of BBA. A parameterisation has been developed to simulate this sub-grid process4, and previously implemented in WRF-Chem using a modal aerosol scheme5. For this work we have modified the WRF-Chem model to simulate 3BEM emissions using the MOSAIC sectional aerosol scheme6. This modified version of WRF-Chem v3.4.1 has been run for September 2012 over South America (25km grid-spacing). We will present model results evaluating the modelled aerosol vertical distribution, size distribution, composition and optical properties against measurements taken by the FAAM BAe-146 research aircraft during the SAMBBA field campaign. The plume-rise parameterisation was found to inject flaming emissions too high over most fires, resulting in high modelled aerosol loadings at high altitude. We probed the behaviour of the parameterisation by developing a new SAMBBA-tuned 3BEM emissions scenario, which uses more realistic estimates of fire size. Results from high-resolution (5 and 1km) nested simulations will also be presented, in order to evaluate the impacts of explicit aerosol-cloud interactions in non-parameterised clouds. 1. K. Longo et al., 2010, Atmos. Chem. Phys., 10, 5785-5795. 2. M. O. Andreae and P. Merlot, 2001, Global Biogeochem. Cy., 15(4), 955-966. 3. S. Freitas et al., 2007, Atmos. Chem. Phys., 7, 3385-3398. 4. G. Grell et al., 2011, Atmos. Chem. Phys., 11, 5289-5303. 5. R. Zavari et al., 2008, J. Geophys. Res., 113, D132024.

Quantifying the Stable Boundary Layer Structure and Evolution during T-REX 2006

DTIC Science & Technology

2014-09-30

integrating surface observations, data from in-situ measurements, and a nested numerical model with two related topics was conducted in this project. the WRF ...as well as quantify differences at a fine scale model output using the different turbulent mixing/diffusion options in the WRF -ARW model; and (2... WRF model planetary boundary layer schemes were also conducted to study a downslope windstorm and rotors in Las Vegas valley. Two events (March 20

An evaluation of the performance of a WRF multi-physics ensemble for heatwave events over the city of Melbourne in southeast Australia

NASA Astrophysics Data System (ADS)

Imran, H. M.; Kala, J.; Ng, A. W. M.; Muthukumaran, S.

2018-04-01

Appropriate choice of physics options among many physics parameterizations is important when using the Weather Research and Forecasting (WRF) model. The responses of different physics parameterizations of the WRF model may vary due to geographical locations, the application of interest, and the temporal and spatial scales being investigated. Several studies have evaluated the performance of the WRF model in simulating the mean climate and extreme rainfall events for various regions in Australia. However, no study has explicitly evaluated the sensitivity of the WRF model in simulating heatwaves. Therefore, this study evaluates the performance of a WRF multi-physics ensemble that comprises 27 model configurations for a series of heatwave events in Melbourne, Australia. Unlike most previous studies, we not only evaluate temperature, but also wind speed and relative humidity, which are key factors influencing heatwave dynamics. No specific ensemble member for all events explicitly showed the best performance, for all the variables, considering all evaluation metrics. This study also found that the choice of planetary boundary layer (PBL) scheme had largest influence, the radiation scheme had moderate influence, and the microphysics scheme had the least influence on temperature simulations. The PBL and microphysics schemes were found to be more sensitive than the radiation scheme for wind speed and relative humidity. Additionally, the study tested the role of Urban Canopy Model (UCM) and three Land Surface Models (LSMs). Although the UCM did not play significant role, the Noah-LSM showed better performance than the CLM4 and NOAH-MP LSMs in simulating the heatwave events. The study finally identifies an optimal configuration of WRF that will be a useful modelling tool for further investigations of heatwaves in Melbourne. Although our results are invariably region-specific, our results will be useful to WRF users investigating heatwave dynamics elsewhere.

Development of WRF-CO2 4DVAR Data Assimilation System

NASA Astrophysics Data System (ADS)

Zheng, T.; French, N. H. F.

2016-12-01

Four dimensional variational (4DVar) assimilation systems have been widely used for CO2 inverse modeling at global scale. At regional scale, however, 4DVar assimilation systems have been lacking. At present, most regional CO2 inverse models use Lagrangian particle backward trajectory tools to compute influence function in an analytical/synthesis framework. To provide a 4DVar based alternative, we developed WRF-CO2 4DVAR based on Weather Research and Forecasting (WRF), its chemistry extension (WRF-Chem), and its data assimilation system (WRFDA/WRFPLUS). Different from WRFDA, WRF-CO2 4DVAR does not optimize meteorology initial condition, instead it solves for the optimized CO2 surface fluxes (sources/sink) constrained by atmospheric CO2 observations. Based on WRFPLUS, we developed tangent linear and adjoint code for CO2 emission, advection, vertical mixing in boundary layer, and convective transport. Furthermore, we implemented an incremental algorithm to solve for optimized CO2 emission scaling factors by iteratively minimizing the cost function in a Bayes framework. The model sensitivity (of atmospheric CO2 with respect to emission scaling factor) calculated by tangent linear and adjoint model agrees well with that calculated by finite difference, indicating the validity of the newly developed code. The effectiveness of WRF-CO2 4DVar for inverse modeling is tested using forward-model generated pseudo-observation data in two experiments: first-guess CO2 fluxes has a 50% overestimation in the first case and 50% underestimation in the second. In both cases, WRF-CO2 4DVar reduces cost function to less than 10-4 of its initial values in less than 20 iterations and successfully recovers the true values of emission scaling factors. We expect future applications of WRF-CO2 4DVar with satellite observations will provide insights for CO2 regional inverse modeling, including the impacts of model transport error in vertical mixing.

Investigation of the Representation of OLEs and Terrain Effects Within the Costal Zone in the EDMF Parameterization Scheme: An Airborne Doppler Wind Lidar Perspective

DTIC Science & Technology

2014-10-20

have received several versions of the EDMF from Joao Teixeira for testing . RESULTS Most of the results of our last year’s research effort were...as a comparison to the ABL over cold water. Note that the MATERHORN was co-funded by ONR (Ferek). As this research has progressed, we have added a...transports. Emmitt and de Wekker are using the WRF and COAMPs models to test out sensitivities to changes in the EDMF related to our field data. We

Evaluation of WRF physical parameterizations against ARM/ASR Observations in the post-cold-frontal region to improve low-level clouds representation in CAM5

NASA Astrophysics Data System (ADS)

Lamraoui, F.; Booth, J. F.; Naud, C. M.

2017-12-01

The representation of subgrid-scale processes of low-level marine clouds located in the post-cold-frontal region poses a serious challenge for climate models. More precisely, the boundary layer parameterizations are predominantly designed for individual regimes that can evolve gradually over time and does not accommodate the cold front passage that can overly modify the boundary layer rapidly. Also, the microphysics schemes respond differently to the quick development of the boundary layer schemes, especially under unstable conditions. To improve the understanding of cloud physics in the post-cold frontal region, the present study focuses on exploring the relationship between cloud properties, the local processes and large-scale conditions. In order to address these questions, we explore the WRF sensitivity to the interaction between various combinations of the boundary layer and microphysics parameterizations, including the Community Atmospheric Model version 5 (CAM5) physical package in a perturbed physics ensemble. Then, we evaluate these simulations against ground-based ARM observations over the Azores. The WRF-based simulations demonstrate particular sensitivities of the marine cold front passage and the associated post-cold frontal clouds to the domain size, the resolution and the physical parameterizations. First, it is found that in multiple different case studies the model cannot generate the cold front passage when the domain size is larger than 3000 km2. Instead, the modeled cold front stalls, which shows the importance of properly capturing the synoptic scale conditions. The simulation reveals persistent delay in capturing the cold front passage and also an underestimated duration of the post-cold-frontal conditions. Analysis of the perturbed physics ensemble shows that changing the microphysics scheme leads to larger differences in the modeled clouds than changing the boundary layer scheme. The in-cloud heating tendencies are analyzed to explain this sensitivity.

Quantifying errors in surface ozone predictions associated with clouds over the CONUS: a WRF-Chem modeling study using satellite cloud retrievals

NASA Astrophysics Data System (ADS)

Ryu, Young-Hee; Hodzic, Alma; Barre, Jerome; Descombes, Gael; Minnis, Patrick

2018-05-01

Clouds play a key role in radiation and hence O3 photochemistry by modulating photolysis rates and light-dependent emissions of biogenic volatile organic compounds (BVOCs). It is not well known, however, how much error in O3 predictions can be directly attributed to error in cloud predictions. This study applies the Weather Research and Forecasting with Chemistry (WRF-Chem) model at 12 km horizontal resolution with the Morrison microphysics and Grell 3-D cumulus parameterization to quantify uncertainties in summertime surface O3 predictions associated with cloudiness over the contiguous United States (CONUS). All model simulations are driven by reanalysis of atmospheric data and reinitialized every 2 days. In sensitivity simulations, cloud fields used for photochemistry are corrected based on satellite cloud retrievals. The results show that WRF-Chem predicts about 55 % of clouds in the right locations and generally underpredicts cloud optical depths. These errors in cloud predictions can lead to up to 60 ppb of overestimation in hourly surface O3 concentrations on some days. The average difference in summertime surface O3 concentrations derived from the modeled clouds and satellite clouds ranges from 1 to 5 ppb for maximum daily 8 h average O3 (MDA8 O3) over the CONUS. This represents up to ˜ 40 % of the total MDA8 O3 bias under cloudy conditions in the tested model version. Surface O3 concentrations are sensitive to cloud errors mainly through the calculation of photolysis rates (for ˜ 80 %), and to a lesser extent to light-dependent BVOC emissions. The sensitivity of surface O3 concentrations to satellite-based cloud corrections is about 2 times larger in VOC-limited than NOx-limited regimes. Our results suggest that the benefits of accurate predictions of cloudiness would be significant in VOC-limited regions, which are typical of urban areas.

Evaluation and Improvement of Polar WRF simulations using the observed atmospheric profiles in the Arctic seasonal ice zone

NASA Astrophysics Data System (ADS)

Liu, Z.; Schweiger, A. J. B.

2016-12-01

We use the Polar Weather Research and Forecasting (WRF) model to simulate atmospheric conditions during the Seasonal Ice Zone Reconnaissance Survey (SIZRS) over the Beaufort Sea in the summer since 2013. With the 119 SIZRS dropsondes in the18 cross sections along the 150W and 140W longitude lines, we evaluate the performance of WRF simulations and two forcing data sets, the ERA-Interim reanalysis and the Global Forecast System (GFS) analysis, and explore the improvement of the Polar WRF performance when the dropsonde data are assimilated using observation nudging. Polar WRF, ERA-Interim, and GFS can reproduce the general features of the observed mean atmospheric profiles, such as low-level temperature inversion, low-level jet (LLJ) and specific humidity inversion. The Polar WRF significantly improves the mean LLJ, with a lower and stronger jet and a larger turning angle than the forcing, which is likely related to the lower values of the boundary layer diffusion in WRF than in the global models such as ECMWF and GFS. The Polar WRF simulated relative humidity closely resembles the forcing datasets while having large biases compared to observations. This suggests that the performance of Polar WRF and its forecasts in this region are limited by the quality of the forcing dataset and that the assimilation of more and better-calibrated observations, such as humidity data, is critical for their improvement. We investigate the potential of assimilating the SIZRS dropsonde dataset in improving the weather forecast over the Beaufort Sea. A simple local nudging approach is adopted. Along SIZRS flight cross sections, a set of Polar WRF simulations are performed with varying number of variables and dropsonde profiles assimilated. Different model physics are tested to examine the sensitivity of different aspects of model physics, such as boundary layer schemes, cloud microphysics, and radiation parameterization, to data assimilation. The comparison of the Polar WRF runs with assimilation and the runs without assimilation demonstrates the importance of SIZRS dropsonde data to the improvement of atmospheric analysis and reanalysis such as GFS and ERA-Interim, and consequently to the improvement of weather forecast in this region.

Plasma Neutrophil Gelatinase-Associated Lipocalin and Predicting Clinically Relevant Worsening Renal Function in Acute Heart Failure

PubMed Central

Damman, Kevin; A.E. Valente, Mattia; J. van Veldhuisen, Dirk; G.F. Cleland, John; M. O’Connor, Christopher; Metra, Marco; Ponikowski, Piotr; Cotter, Gad; Davison, Beth; M. Givertz, Michael; M. Bloomfield, Daniel; L. Hillege, Hans; A. Voors, Adriaan

2017-01-01

The aim of this study was to evaluate the ability of Neutrophil Gelatinase-Associated Lipocalin (NGAL) to predict clinically relevant worsening renal function (WRF) in acute heart failure (AHF). Plasma NGAL and serum creatinine changes during the first 4 days of admission were investigated in 1447 patients hospitalized for AHF and enrolled in the Placebo-Controlled Randomized Study of the Selective A1Adenosine Receptor Antagonist Rolofylline for Patients Hospitalized with Acute Decompensated Heart Failure and Volume Overload to Assess Treatment Effect on Congestion and Renal Function (PROTECT) study. WRF was defined as serum creatinine rise ≥ 0.3 mg/dL through day 4. Biomarker patterns were described using linear mixed models. WRF developed in 325 patients (22%). Plasma NGAL did not rise earlier than creatinine in patients with WRF. After multivariable adjustment, baseline plasma NGAL, but not creatinine, predicted WRF. AUCs for WRF prediction were modest (<0.60) for all models. NGAL did not independently predict death or rehospitalization (p = n.s.). Patients with WRF and high baseline plasma NGAL had a greater risk of death, and renal or cardiovascular rehospitalization by 60 days than patients with WRF and a low baseline plasma NGAL (p for interaction = 0.024). A rise in plasma NGAL after baseline was associated with a worse outcome in patients with WRF, but not in patients without WRF (p = 0.007). On the basis of these results, plasma NGAL does not provide additional, clinically relevant information about the occurrence of WRF in patients with AHF. PMID:28698481

Plasma Neutrophil Gelatinase-Associated Lipocalin and Predicting Clinically Relevant Worsening Renal Function in Acute Heart Failure.

PubMed

Damman, Kevin; Valente, Mattia A E; van Veldhuisen, Dirk J; Cleland, John G F; O'Connor, Christopher M; Metra, Marco; Ponikowski, Piotr; Cotter, Gad; Davison, Beth; Givertz, Michael M; Bloomfield, Daniel M; Hillege, Hans L; Voors, Adriaan A

2017-07-08

The aim of this study was to evaluate the ability of Neutrophil Gelatinase-Associated Lipocalin (NGAL) to predict clinically relevant worsening renal function (WRF) in acute heart failure (AHF). Plasma NGAL and serum creatinine changes during the first 4 days of admission were investigated in 1447 patients hospitalized for AHF and enrolled in the Placebo-Controlled Randomized Study of the Selective A₁Adenosine Receptor Antagonist Rolofylline for Patients Hospitalized with Acute Decompensated Heart Failure and Volume Overload to Assess Treatment Effect on Congestion and Renal Function (PROTECT) study. WRF was defined as serum creatinine rise ≥ 0.3 mg/dL through day 4. Biomarker patterns were described using linear mixed models. WRF developed in 325 patients (22%). Plasma NGAL did not rise earlier than creatinine in patients with WRF. After multivariable adjustment, baseline plasma NGAL, but not creatinine, predicted WRF. AUCs for WRF prediction were modest (<0.60) for all models. NGAL did not independently predict death or rehospitalization ( p = n.s.). Patients with WRF and high baseline plasma NGAL had a greater risk of death, and renal or cardiovascular rehospitalization by 60 days than patients with WRF and a low baseline plasma NGAL (p for interaction = 0.024). A rise in plasma NGAL after baseline was associated with a worse outcome in patients with WRF, but not in patients without WRF ( p = 0.007). On the basis of these results, plasma NGAL does not provide additional, clinically relevant information about the occurrence of WRF in patients with AHF.

NASA SPoRT Modeling and Data Assimilation Research and Transition Activities Using WRF, LIS and GSI

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; Blankenship, Clay B.; Zavodsky, Bradley T.; Srikishen, Jayanthi; Berndt, Emily B.

2014-01-01

weather research and forecasting ===== The NASA Short-term Prediction Research and Transition (SPoRT) program has numerous modeling and data assimilation (DA) activities in which the WRF model is a key component. SPoRT generates realtime, research satellite products from the MODIS and VIIRS instruments, making the data available to NOAA/NWS partners running the WRF/EMS, including: (1) 2-km northwestern-hemispheric SST composite, (2) daily, MODIS green vegetation fraction (GVF) over CONUS, and (3) NASA Land Information System (LIS) runs of the Noah LSM over the southeastern CONUS. Each of these datasets have been utilized by specific SPoRT partners in local EMS model runs, with select offices evaluating the impacts using a set of automated scripts developed by SPoRT that manage data acquisition and run the NCAR Model Evaluation Tools verification package. SPoRT is engaged in DA research with the Gridpoint Statistical Interpolation (GSI) and Ensemble Kalman Filter in LIS for soil moisture DA. Ongoing DA projects using GSI include comparing the impacts of assimilating Atmospheric Infrared Sounder (AIRS) radiances versus retrieved profiles, and an analysis of extra-tropical cyclones with intense non-convective winds. As part of its Early Adopter activities for the NASA Soil Moisture Active Passive (SMAP) mission, SPoRT is conducting bias correction and soil moisture DA within LIS to improve simulations using the NASA Unified-WRF (NU-WRF) for both the European Space Agency's Soil Moisture Ocean Salinity and upcoming SMAP mission data. SPoRT has also incorporated real-time global GVF data into LIS and WRF from the VIIRS product being developed by NOAA/NESDIS. This poster will highlight the research and transition activities SPoRT conducts using WRF, NU-WRF, EMS, LIS, and GSI.

Implementing Network Common Data Form (netCDF) for the 3DWF Model

DTIC Science & Technology

2016-02-01

format. In addition, data extraction from netCDF-formatted Weather Research and Forecasting ( WRF ) model results necessary for the 3DWF model’s wind...Requirement for the 3DWF Model 1 3. Implementing netCDF to the 3DWF Model 2 3.1 Weather Research and Forecasting ( WRF ) domain and results 3 3.2...Extracting Variables from netCDF Formatted WRF Data File 5 3.3 Converting the 3DWF’s Results into netCDF 11 4. Conclusion 14 5. References 15 Appendix

Using the Random Nearest Neighbor Data Mining Method to Extract Maximum Information Content from Weather Forecasts from Multiple Predictors of Weather and One Predictand (Low-Level Turbulence)

DTIC Science & Technology

2014-10-30

Force Weather Agency (AFWA) WRF 15-km atmospheric model forecast data and low-level turbulence. Archives of historical model data forecast predictors...Relationships between WRF model predictors and PIREPS were developed using the new data mining methodology. The new methodology was inspired...convection. Predictors of turbulence were collected from the AFWA WRF 15km model, and corresponding PIREPS (the predictand) were collected between 2013

A Coupled Surface Nudging Scheme for use in Retrospective Weather and Climate Simulations for Environmental Applications

EPA Science Inventory

A surface analysis nudging scheme coupling atmospheric and land surface thermodynamic parameters has been implemented into WRF v3.8 (latest version) for use with retrospective weather and climate simulations, as well as for applications in air quality, hydrology, and ecosystem mo...

Improved cyberinfrastructure for integrated hydrometeorological predictions within the fully-coupled WRF-Hydro modeling system

NASA Astrophysics Data System (ADS)

gochis, David; hooper, Rick; parodi, Antonio; Jha, Shantenu; Yu, Wei; Zaslavsky, Ilya; Ganapati, Dinesh

2014-05-01

The community WRF-Hydro system is currently being used in a variety of flood prediction and regional hydroclimate impacts assessment applications around the world. Despite its increasingly wide use certain cyberinfrastructure bottlenecks exist in the setup, execution and post-processing of WRF-Hydro model runs. These bottlenecks result in wasted time, labor, data transfer bandwidth and computational resource use. Appropriate development and use of cyberinfrastructure to setup and manage WRF-Hydro modeling applications will streamline the entire workflow of hydrologic model predictions. This talk will present recent advances in the development and use of new open-source cyberinfrastructure tools for the WRF-Hydro architecture. These tools include new web-accessible pre-processing applications, supercomputer job management applications and automated verification and visualization applications. The tools will be described successively and then demonstrated in a set of flash flood use cases for recent destructive flood events in the U.S. and in Europe. Throughout, an emphasis on the implementation and use of community data standards for data exchange is made.

The Another Assimilation System for WRF-Chem (AAS4WRF): a new mass-conserving emissions pre-processor for WRF-Chem regional modelling

NASA Astrophysics Data System (ADS)

Vara Vela, A. L.; Muñoz, A.; Lomas, A., Sr.; González, C. M.; Calderon, M. G.; Andrade, M. D. F.

2017-12-01

The Weather Research and Forecasting with Chemistry (WRF-Chem) community model have been widely used for the study of pollutants transport, formation of secondary pollutants, as well as for the assessment of air quality policies implementation. A key factor to improve the WRF-Chem air quality simulations over urban areas is the representation of anthropogenic emission sources. There are several tools that are available to assist users in creating their own emissions based on global emissions information (e.g. anthro_emiss, prep_chem_src); however, there is no single tool that will construct local emissions input datasets for any particular domain at this time. Because the official emissions pre-processor (emiss_v03) is designed to work with domains located over North America, this work presents the Another Assimilation System for WRF-Chem (AAS4WRF), a ncl based mass-conserving emissions pre-processor designed to create WRF-Chem ready emissions files from local inventories on a lat/lon projection. AAS4WRF is appropriate to scale emission rates from both surface and elevated sources, providing the users an alternative way to assimilate their emissions to WRF-Chem. Since it was successfully tested for the first time for the city of Lima, Peru in 2014 (managed by SENAMHI, the National Weather Service of the country), several studies on air quality modelling have applied this utility to convert their emissions to those required for WRF-Chem. Two case studies performed in the metropolitan areas of Sao Paulo and Manizales in Brazil and Colombia, respectively, are here presented in order to analyse the influence of using local or global emission inventories in the representation of regulated air pollutants such as O3 and PM2.5. Although AAS4WRF works with local emissions information at the moment, further work is being conducted to make it compatible with global/regional emissions data file format. The tool is freely available upon request to the corresponding author.

Coupled Atmosphere-Surface Modeling of Lake Levels of the North American Great Lakes under Climate Change

NASA Astrophysics Data System (ADS)

Lofgren, B. M.; Xiao, C.

2016-12-01

The influence of projected climate change on the water levels of the Great Lakes is subject to considerable uncertainty, and methods that have long been used to determine this sensitivity have been discredited. A strong candidate, albeit expensive, to replace problematic methods is to use outputs that result from dynamical downscaling of future climate simulations, focused on the hydroclimate of the Great Lakes basin. We have produced initial estimates of Great Lakes water levels in the mid- and late 21st century using the Weather Research and Forecasting (WRF) model, including its lake module, driven by lateral boundary conditions from the Geophysical Fluid Dynamics Lab Climate Model version 3.0 (GFDL CM3), under RCP4.5 and 8.5 scenarios. Future lake levels are influenced by the balance between projected general increases in precipitation and increases in evapotranspiration from both land and lake in the basin, driven primarily by the surface radiative energy budget and secondarily by air temperature. The net result was drops in lake level of up to 15 cm, in contrast to the results from much-used older methods, which often projected drops exceeding 1 m. Future plans include increased detail in the simulation of water flow overland and in river channels using WRF-Hydro, and full coupling of regional atmospheric systems with 3-dimensional dynamical lake implementation of the Finite Volume Community Ocean Model (FVCOM).

Improved simulation of precipitation in the tropics using a modified BMJ scheme in the WRF model

NASA Astrophysics Data System (ADS)

Fonseca, R. M.; Zhang, T.; Yong, K.-T.

2015-09-01

The successful modelling of the observed precipitation, a very important variable for a wide range of climate applications, continues to be one of the major challenges that climate scientists face today. When the Weather Research and Forecasting (WRF) model is used to dynamically downscale the Climate Forecast System Reanalysis (CFSR) over the Indo-Pacific region, with analysis (grid-point) nudging, it is found that the cumulus scheme used, Betts-Miller-Janjić (BMJ), produces excessive rainfall suggesting that it has to be modified for this region. Experimentation has shown that the cumulus precipitation is not very sensitive to changes in the cloud efficiency but varies greatly in response to modifications of the temperature and humidity reference profiles. A new version of the scheme, denoted "modified BMJ" scheme, where the humidity reference profile is more moist, was developed. In tropical belt simulations it was found to give a better estimate of the observed precipitation as given by the Tropical Rainfall Measuring Mission (TRMM) 3B42 data set than the default BMJ scheme for the whole tropics and both monsoon seasons. In fact, in some regions the model even outperforms CFSR. The advantage of modifying the BMJ scheme to produce better rainfall estimates lies in the final dynamical consistency of the rainfall with other dynamical and thermodynamical variables of the atmosphere.

Assessing the applicability of WRF optimal parameters under the different precipitation simulations in the Greater Beijing Area

NASA Astrophysics Data System (ADS)

Di, Zhenhua; Duan, Qingyun; Wang, Chen; Ye, Aizhong; Miao, Chiyuan; Gong, Wei

2018-03-01

Forecasting skills of the complex weather and climate models have been improved by tuning the sensitive parameters that exert the greatest impact on simulated results based on more effective optimization methods. However, whether the optimal parameter values are still work when the model simulation conditions vary, which is a scientific problem deserving of study. In this study, a highly-effective optimization method, adaptive surrogate model-based optimization (ASMO), was firstly used to tune nine sensitive parameters from four physical parameterization schemes of the Weather Research and Forecasting (WRF) model to obtain better summer precipitation forecasting over the Greater Beijing Area in China. Then, to assess the applicability of the optimal parameter values, simulation results from the WRF model with default and optimal parameter values were compared across precipitation events, boundary conditions, spatial scales, and physical processes in the Greater Beijing Area. The summer precipitation events from 6 years were used to calibrate and evaluate the optimal parameter values of WRF model. Three boundary data and two spatial resolutions were adopted to evaluate the superiority of the calibrated optimal parameters to default parameters under the WRF simulations with different boundary conditions and spatial resolutions, respectively. Physical interpretations of the optimal parameters indicating how to improve precipitation simulation results were also examined. All the results showed that the optimal parameters obtained by ASMO are superior to the default parameters for WRF simulations for predicting summer precipitation in the Greater Beijing Area because the optimal parameters are not constrained by specific precipitation events, boundary conditions, and spatial resolutions. The optimal values of the nine parameters were determined from 127 parameter samples using the ASMO method, which showed that the ASMO method is very highly-efficient for optimizing WRF model parameters.

The SPoRT-WRF: Evaluating the Impact of NASA Datasets on Convective Forecasts

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley; Case, Jonathan; Kozlowski, Danielle; Molthan, Andrew

2012-01-01

The Short-term Prediction Research and Transition Center (SPoRT) is a collaborative partnership between NASA and operational forecasting entities, including a number of National Weather Service offices. SPoRT transitions real-time NASA products and capabilities to its partners to address specific operational forecast challenges. One challenge that forecasters face is applying convection-allowing numerical models to predict mesoscale convective weather. In order to address this specific forecast challenge, SPoRT produces real-time mesoscale model forecasts using the Weather Research and Forecasting (WRF) model that includes unique NASA products and capabilities. Currently, the SPoRT configuration of the WRF model (SPoRT-WRF) incorporates the 4-km Land Information System (LIS) land surface data, 1-km SPoRT sea surface temperature analysis and 1-km Moderate resolution Imaging Spectroradiometer (MODIS) greenness vegetation fraction (GVF) analysis, and retrieved thermodynamic profiles from the Atmospheric Infrared Sounder (AIRS). The LIS, SST, and GVF data are all integrated into the SPoRT-WRF through adjustments to the initial and boundary conditions, and the AIRS data are assimilated into a 9-hour SPoRT WRF forecast each day at 0900 UTC. This study dissects the overall impact of the NASA datasets and the individual surface and atmospheric component datasets on daily mesoscale forecasts. A case study covering the super tornado outbreak across the Ce ntral and Southeastern United States during 25-27 April 2011 is examined. Three different forecasts are analyzed including the SPoRT-WRF (NASA surface and atmospheric data), the SPoRT WRF without AIRS (NASA surface data only), and the operational National Severe Storms Laboratory (NSSL) WRF (control with no NASA data). The forecasts are compared qualitatively by examining simulated versus observed radar reflectivity. Differences between the simulated reflectivity are further investigated using convective parameters along with model soundings to determine the impacts of the various NASA datasets. Additionally, quantitative evaluation of select meteorological parameters is performed using the Meteorological Evaluation Tools model verification package to compare forecasts to in situ surface and upper air observations.

The prognostic importance of worsening renal function during an acute myocardial infarction on long-term mortality.

PubMed

Amin, Amit P; Spertus, John A; Reid, Kimberly J; Lan, Xiao; Buchanan, Donna M; Decker, Carole; Masoudi, Frederick A

2010-12-01

Although an acute worsening in renal function (WRF) commonly occurs among patients hospitalized for acute myocardial infarction (AMI), its long-term prognostic significance is unknown. We examined predictors of WRF and its association with 4-year mortality. Acute myocardial infarction patients from the multicenter PREMIER study (N=2,098) who survived to hospital discharge were followed for at least 4 years. Worsening in renal function was defined as an increase in creatinine during hospitalization of ≥0.3 mg/dL above the admission value. Correlates of WRF were determined with multivariable logistic regression models and used, along with other important clinical covariates, in Cox proportional hazards models to define the independent association between WRF and mortality. Worsening in renal function was observed in 393 (18.7%) of AMI survivors. Diabetes, left ventricular systolic dysfunction, and a history of chronic kidney disease (documented history of renal failure with baseline creatinine>2.5 mg/dL) were independently associated with WRF. During 4-year follow-up, 386 (18.6%) patients died. Mortality was significantly higher in the WRF group (36.6% vs 14.4% in those without WRF, P<.001). After adjusting for other factors associated with WRF and long-term mortality, including baseline creatinine, WRF was independently associated with a higher risk of death (hazard ratio=1.64, 95% CI 1.23-2.19). Worsening in renal function occurs in approximately 1 of 6 AMI survivors and is independently associated with an adverse long-term prognosis. Further studies on interventions to minimize WRF or to more aggressively treat patients developing WRF should be tested. Copyright © 2010 Mosby, Inc. All rights reserved.

The Local Ensemble Transform Kalman Filter with the Weather Research and Forecasting Model: Experiments with Real Observations

NASA Astrophysics Data System (ADS)

Miyoshi, Takemasa; Kunii, Masaru

2012-03-01

The local ensemble transform Kalman filter (LETKF) is implemented with the Weather Research and Forecasting (WRF) model, and real observations are assimilated to assess the newly-developed WRF-LETKF system. The WRF model is a widely-used mesoscale numerical weather prediction model, and the LETKF is an ensemble Kalman filter (EnKF) algorithm particularly efficient in parallel computer architecture. This study aims to provide the basis of future research on mesoscale data assimilation using the WRF-LETKF system, an additional testbed to the existing EnKF systems with the WRF model used in the previous studies. The particular LETKF system adopted in this study is based on the system initially developed in 2004 and has been continuously improved through theoretical studies and wide applications to many kinds of dynamical models including realistic geophysical models. Most recent and important improvements include an adaptive covariance inflation scheme which considers the spatial and temporal inhomogeneity of inflation parameters. Experiments show that the LETKF successfully assimilates real observations and that adaptive inflation is advantageous. Additional experiments with various ensemble sizes show that using more ensemble members improves the analyses consistently.

Assessment of the Sensitivity to the Thermal Roughness Length in Noah and Noah-MP Land Surface Model Using WRF in an Arid Region

NASA Astrophysics Data System (ADS)

Weston, Michael; Chaouch, Naira; Valappil, Vineeth; Temimi, Marouane; Ek, Michael; Zheng, Weizhong

2018-06-01

Atmospheric models are known to underestimate land surface temperature and, by association, 2 m air temperature over dry arid regions during the day due to the treatment of the thermal roughness length also known as roughness length of heat. The thermal roughness length can be controlled by the Zilitinkevich parameter, known as Czil, which is a tunable parameter within the models. Three different scenarios with the WRF model are run to test the impact of the Czil parameter on the simulations using two land surface models: the Noah and Noah-MP models. In this study, a modified version of the Noah-MP model is tested, in which the Czil parameter, and, therefore, the thermal roughness length varies depending on the land cover and vegetation height. The model domain is over the United Arab Emirates (UAE) where the major land cover type is desert. The following configurations are tested: the Noah model with Czil = 0.1, Noah model with Czil = 0.5 and the Noah-MP model with Czil = 0.5 over desert. Results of 2 m air temperature are verified against three stations in the UAE. Mean gross error of the diurnal 2 m temperature was reduced by up to 1.48 and 1.54 °C in the 24 and 48 h forecasts, respectively. This reduced the cold bias in the model. This improvement in air temperature showed to improve the diurnal cycle of relative humidity at the three monitoring stations as well as the duration of the sea breeze in some cases.

Verification of a Non-Hydrostatic Dynamical Core Using Horizontally Spectral Element Vertically Finite Difference Method: 2D Aspects

DTIC Science & Technology

2014-04-01

hydrostatic pressure vertical coordinate, which are the same as those used in the Weather Research and Forecasting ( WRF ) model, but a hybrid sigma...hydrostatic pressure vertical coordinate, which are the 33 same as those used in the Weather Research and Forecasting ( WRF ) model, but a hybrid 34 sigma...Weather Research and Forecasting 79 ( WRF ) Model. The Euler equations are in flux form based on the hydrostatic pressure vertical 80 coordinate. In

The polar WRF downscaled historical and projected 21st century climate for the coast and foothills of Arctic Alaska

NASA Astrophysics Data System (ADS)

Cai, Lei; Alexeev, Vladimir A.; Arp, Christopher D.; Jones, Benjamin M.; Liljedahl, Anna K.; Gädeke, Anne

2018-01-01

Climate change is most pronounced in the northern high latitude region. Yet, climate observations are unable to fully capture regional-scale dynamics due to the sparse weather station coverage, which limits our ability to make reliable climate-based assessments. A set of simulated data products was therefore developed for the North Slope of Alaska through a dynamical downscaling approach. The polar-optimized Weather Research & Forecast (Polar WRF) model was forced by three sources: The ERA-interim reanalysis data (for 1979-2014), the Community Earth System Model 1.0 (CESM1.0) historical simulation (for 1950-2005), and the CESM1.0 projected (for 2006-2100) simulations in two Representative Concentration Pathways (RCP4.5 and RCP8.5) scenarios. Climatic variables were produced in a 10-km grid spacing and a 3-hour interval. The ERA-interim forced WRF (ERA-WRF) proves the value of dynamical downscaling, which yields more realistic topographical-induced precipitation and air temperature, as well as corrects underestimations in observed precipitation. In summary, dry and cold biases to the north of the Brooks Range are presented in ERA-WRF, while CESM forced WRF (CESM-WRF) holds wet and warm biases in its historical period. A linear scaling method allowed for an adjustment of the biases, while keeping the majority of the variability and extreme values of modeled precipitation and air temperature. CESM-WRF under RCP 4.5 scenario projects smaller increase in precipitation and air temperature than observed in the historical CESM-WRF product, while the CESM-WRF under RCP8.5 scenario shows larger changes. The fine spatial and temporal resolution, long temporal coverage, and multi-scenario projections jointly make the dataset appropriate to address a myriad of physical and biological changes occurring on the North Slope of Alaska.

Precipitation From a Multiyear Database of Convection-Allowing WRF Simulations

NASA Astrophysics Data System (ADS)

Goines, D. C.; Kennedy, A. D.

2018-03-01

Convection-allowing models (CAMs) have become frequently used for operational forecasting and, more recently, have been utilized for general circulation model downscaling. CAM forecasts have typically been analyzed for a few case studies or over short time periods, but this limits the ability to judge the overall skill of deterministic simulations. Analysis over long time periods can yield a better understanding of systematic model error. Four years of warm season (April-August, 2010-2013)-simulated precipitation has been accumulated from two Weather Research and Forecasting (WRF) models with 4 km grid spacing. The simulations were provided by the National Center for Environmental Prediction (NCEP) and the National Severe Storms Laboratory (NSSL), each with different dynamic cores and parameterization schemes. These simulations are evaluated against the NCEP Stage-IV precipitation data set with similar 4 km grid spacing. The spatial distribution and diurnal cycle of precipitation in the central United States are analyzed using Hovmöller diagrams, grid point correlations, and traditional verification skill scoring (i.e., ETS; Equitable Threat Score). Although NCEP-WRF had a high positive error in total precipitation, spatial characteristics were similar to observations. For example, the spatial distribution of NCEP-WRF precipitation correlated better than NSSL-WRF for the Northern Plains. Hovmöller results exposed a delay in initiation and decay of diurnal precipitation by NCEP-WRF while both models had difficulty in reproducing the timing and location of propagating precipitation. ETS was highest for NSSL-WRF in all domains at all times. ETS was also higher in areas of propagating precipitation compared to areas of unorganized diurnal scattered precipitation. Monthly analysis identified unique differences between the two models in their abilities to correctly simulate the spatial distribution and zonal motion of precipitation through the warm season.

Study of atmospheric condition during the heavy rain event in Bojonegoro using weather research and forecasting (WRF) model: case study 9 February 2017

NASA Astrophysics Data System (ADS)

Saragih, I. J. A.; Meygatama, A. G.; Sugihartati, F. M.; Sidauruk, M.; Mulsandi, A.

2018-03-01

During 2016, there are frequent heavy rains in the Bojonegoro region, one of which is rain on 9 February 2016. The occurrence of heavy rainfall can cause the floods that inundate the settlements, rice fields, roads, and public facilities. This makes it important to analyze the atmospheric conditions during the heavy rainfall events in Bojonegoro. One of the analytical methods that can be used is using WRF-Advanced Research WRF (WRF-ARW) model. This study was conducted by comparing the rain analysis from WRF-ARW model with the Himawari-8 satellite imagery. The data used are Final Analysis (FNL) data for the WRF-ARW model and infrared (IR) channel for Himawari-8 satellite imagery. The data are processed into the time-series images and then analyzed descriptively. The meteorological parameters selected to be analyzed are relative humidity, vortices, divergences, air stability index, and precipitation. These parameters are expected to indicate the existence of a convective activity in Bojonegoro during the heavy rainfall event. The Himawari-8 satellite imagery shows that there is a cluster of convective clouds in Bojonegoro during the heavy rainfall event. The lowest value of the cloud top temperature indicates that the cluster of convective clouds is a cluster of Cumulonimbus cloud (CB).

Coupling fast all-season soil strength land surface model with weather research and forecasting model to assess low-level icing in complex terrain

NASA Astrophysics Data System (ADS)

Sines, Taleena R.

Icing poses as a severe hazard to aircraft safety with financial resources and even human lives hanging in the balance when the decision to ground a flight must be made. When analyzing the effects of ice on aviation, a chief cause for danger is the disruption of smooth airflow, which increases the drag force on the aircraft therefore decreasing its ability to create lift. The Weather Research and Forecast (WRF) model Advanced Research WRF (WRF-ARW) is a collaboratively created, flexible model designed to run on distributed computing systems for a variety of applications including forecasting research, parameterization research, and real-time numerical weather prediction. Land-surface models, one of the physics options available in the WRF-ARW, output surface heat and moisture flux given radiation, precipitation, and surface properties such as soil type. The Fast All-Season Soil STrength (FASST) land-surface model was developed by the U.S. Army ERDC-CRREL in Hanover, New Hampshire. Designed to use both meteorological and terrain data, the model calculates heat and moisture within the surface layer as well as the exchange of these parameters between the soil, surface elements (such as snow and vegetation), and atmosphere. Focusing on the Presidential Mountain Range of New Hampshire under the NASA Experimental Program to Stimulate Competitive Research (EPSCoR) Icing Assessments in Cold and Alpine Environments project, one of the main goals is to create a customized, high resolution model to predict and assess ice accretion in complex terrain. The purpose of this research is to couple the FASST land-surface model with the WRF to improve icing forecasts in complex terrain. Coupling FASST with the WRF-ARW may improve icing forecasts because of its sophisticated approach to handling processes such as meltwater, freezing, thawing, and others that would affect the water and energy budget and in turn affect icing forecasts. Several transformations had to take place in order for the FASST land-surface model and WRF-ARW to work together as fully coupled models. Changes had to be made to the WRF-ARW build mechanisms (Chapter 1, section a) so that FASST would be recognized as a new option that could be chosen through the namelist and compiled with other modules. Similarly, FASST had to be altered to no longer read meteorological data from a file, but accept input from WRF-ARW at each time step in a way that did not alter the integrity or run-time processes of the model. Several icing events were available to test the newly coupled model as well as the performance of other available land-surface models from the WRF-ARW. A variation of event intensities and durations from these events were chosen to give a broader view of the land-surface models' abilities to accurately predict icing in complex terrain. Non- icing events were also used in testing to ensure the land-surface models were not predicting ice in the events where none occurred. When compared to the other land-surface models and observations FASST showed a warm bias in several regions. As the forecasts progressed, FASST appeared to attempt to correct this bias and performed similarly to the other land-surface models and at times better than these land-surface models in areas of the domain not affected by this bias. To correct this warm bias, future investigation should be conducted into the reasoning behind this warm bias, including but not limited to: FASST operation and elevation modeling, WRF-ARW variables and forecasting methods, as well as allowing for spin-up prior to forecast times. Following the correction to the warm bias, FASST can be parallelized to allow for operational forecast performance and included in the WRF-ARW forecasting suite for future software releases. (Abstract shortened by UMI.).

WRF model sensitivity to choice of parameterization: a study of the `York Flood 1999'

NASA Astrophysics Data System (ADS)

Remesan, Renji; Bellerby, Tim; Holman, Ian; Frostick, Lynne

2015-10-01

Numerical weather modelling has gained considerable attention in the field of hydrology especially in un-gauged catchments and in conjunction with distributed models. As a consequence, the accuracy with which these models represent precipitation, sub-grid-scale processes and exceptional events has become of considerable concern to the hydrological community. This paper presents sensitivity analyses for the Weather Research Forecast (WRF) model with respect to the choice of physical parameterization schemes (both cumulus parameterisation (CPSs) and microphysics parameterization schemes (MPSs)) used to represent the `1999 York Flood' event, which occurred over North Yorkshire, UK, 1st-14th March 1999. The study assessed four CPSs (Kain-Fritsch (KF2), Betts-Miller-Janjic (BMJ), Grell-Devenyi ensemble (GD) and the old Kain-Fritsch (KF1)) and four MPSs (Kessler, Lin et al., WRF single-moment 3-class (WSM3) and WRF single-moment 5-class (WSM5)] with respect to their influence on modelled rainfall. The study suggests that the BMJ scheme may be a better cumulus parameterization choice for the study region, giving a consistently better performance than other three CPSs, though there are suggestions of underestimation. The WSM3 was identified as the best MPSs and a combined WSM3/BMJ model setup produced realistic estimates of precipitation quantities for this exceptional flood event. This study analysed spatial variability in WRF performance through categorical indices, including POD, FBI, FAR and CSI during York Flood 1999 under various model settings. Moreover, the WRF model was good at predicting high-intensity rare events over the Yorkshire region, suggesting it has potential for operational use.

A study comparison of two system model performance in estimated lifted index over Indonesia.

NASA Astrophysics Data System (ADS)

lestari, Juliana tri; Wandala, Agie

2018-05-01

Lifted index (LI) is one of atmospheric stability indices that used for thunderstorm forecasting. Numerical weather Prediction Models are essential for accurate weather forecast these day. This study has completed the attempt to compare the two NWP models these are Weather Research Forecasting (WRF) model and Global Forecasting System (GFS) model in estimates LI at 20 locations over Indonesia and verified the result with observation. Taylor diagram was used to comparing the models skill with shown the value of standard deviation, coefficient correlation and Root mean square error (RMSE). This study using the dataset on 00.00 UTC and 12.00 UTC during mid-March to Mid-April 2017. From the sample of LI distributions, both models have a tendency to overestimated LI value in almost all region in Indonesia while the WRF models has the better ability to catch the LI pattern distribution with observation than GFS model has. The verification result shows how both WRF and GFS model have such a weak relationship with observation except Eltari meteorologi station that its coefficient correlation reach almost 0.6 with the low RMSE value. Mean while WRF model have a better performance than GFS model. This study suggest that estimated LI of WRF model can provide the good performance for Thunderstorm forecasting over Indonesia in the future. However unsufficient relation between output models and observation in the certain location need a further investigation.

Using a Coupled Lake Model with WRF for Dynamical Downscaling

EPA Science Inventory

The Weather Research and Forecasting (WRF) model is used to downscale a coarse reanalysis (National Centers for Environmental Prediction–Department of Energy Atmospheric Model Intercomparison Project reanalysis, hereafter R2) as a proxy for a global climate model (GCM) to examine...

Can High-resolution WRF Simulations Be Used for Short-term Forecasting of Lightning?

NASA Technical Reports Server (NTRS)

Goodman, S. J.; Lapenta, W.; McCaul, E. W., Jr.; LaCasse, K.; Petersen, W.

2006-01-01

A number of research teams have begun to make quasi-operational forecast simulations at high resolution with models such as the Weather Research and Forecast (WRF) model. These model runs have used horizontal meshes of 2-4 km grid spacing, and thus resolved convective storms explicitly. In the light of recent global satellite-based observational studies that reveal robust relationships between total lightning flash rates and integrated amounts of precipitation-size ice hydrometeors in storms, it is natural to inquire about the capabilities of these convection-resolving models in representing the ice hydrometeor fields faithfully. If they do, this might make operational short-term forecasts of lightning activity feasible. We examine high-resolution WRF simulations from several Southeastern cases for which either NLDN or LMA lightning data were available. All the WRF runs use a standard microphysics package that depicts only three ice species, cloud ice, snow and graupel. The realism of the WRF simulations is examined by comparisons with both lightning and radar observations and with additional even higher-resolution cloud-resolving model runs. Preliminary findings are encouraging in that they suggest that WRF often makes convective storms of the proper size in approximately the right location, but they also indicate that higher resolution and better hydrometeor microphysics would be helpful in improving the realism of the updraft strengths, reflectivity and ice hydrometeor fields.

Spatiotemporal characteristics of heat waves over China in regional climate simulations within the CORDEX-EA project

NASA Astrophysics Data System (ADS)

Wang, Pinya; Tang, Jianping; Sun, Xuguang; Liu, Jianyong; Juan, Fang

2018-03-01

Using the Weather Research and Forecasting (WRF) model, this paper analyzes the spatiotemporal features of heat waves in 20-year regional climate simulations over East Asia, and investigates the capability of WRF to reproduce observational heat waves in China. Within the framework of the Coordinated Regional Climate Downscaling Experiment (CORDEX), the WRF model is driven by the ERA-Interim (ERAIN) reanalysis, and five continuous simulations are conducted from 1989 to 2008. Of these, four runs apply the interior spectral nudging (SN) technique with different wavenumbers, nudging variables and nudging coefficients. Model validations show that WRF can reasonably reproduce the spatiotemporal features of heat waves in China. Compared with the experiment without SN, the application of SN is effectie on improving the skill of the model in simulating both the spatial distributions and temporal variations of heat waves of different intensities. The WRF model shows advantages in reproducing the synoptic circulations with SN and therefore yields better representations for heat wave events. Besides, the SN method is able to preserve the variability of large-scale circulations quite well, which in turn adjusts the extreme temperature variability towards the observation. Among the four SN experiments, those with stronger nudging coefficients perform better in modulating both the spatial and temporal features of heat waves. In contrast, smaller nudging coefficients weaken the effects of SN on improving WRF's performances.

Coupled Stochastic Time-Inverted Lagrangian Transport/Weather Forecast and Research/Vegetation Photosynthesis and Respiration Model. Part II; Simulations of Tower-Based and Airborne CO2 Measurements

NASA Technical Reports Server (NTRS)

Eluszkiewicz, Janusz; Nehrkorn, Thomas; Wofsy, Steven C.; Matross, Daniel; Gerbig, Christoph; Lin, John C.; Freitas, Saulo; Longo, Marcos; Andrews, Arlyn E.; Peters, Wouter

2007-01-01

This paper evaluates simulations of atmospheric CO2 measured in 2004 at continental surface and airborne receptors, intended to test the capability to use data with high temporal and spatial resolution for analyses of carbon sources and sinks at regional and continental scales. The simulations were performed using the Stochastic Time-Inverted Lagrangian Transport (STILT) model driven by the Weather Forecast and Research (WRF) model, and linked to surface fluxes from the satellite-driven Vegetation Photosynthesis and Respiration Model (VPRM). The simulations provide detailed representations of hourly CO2 tower data and reproduce the shapes of airborne vertical profiles with high fidelity. WRF meteorology gives superior model performance compared with standard meteorological products, and the impact of including WRF convective mass fluxes in the STILT trajectory calculations is significant in individual cases. Important biases in the simulation are associated with the nighttime CO2 build-up and subsequent morning transition to convective conditions, and with errors in the advected lateral boundary condition. Comparison of STILT simulations driven by the WRF model against those driven by the Brazilian variant of the Regional Atmospheric Modeling System (BRAMS) shows that model-to-model differences are smaller than between an individual transport model and observations, pointing to systematic errors in the simulated transport. Future developments in the WRF model s data assimilation capabilities, basic research into the fundamental aspects of trajectory calculations, and intercomparison studies involving other transport models, are possible venues for reducing these errors. Overall, the STILT/WRF/VPRM offers a powerful tool for continental and regional scale carbon flux estimates.

High-Resolution Specification of the Land and Ocean Surface for Improving Regional Mesoscale Model Predictions

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; Lazarus, Steven M.; Splitt, Michael E.; Crosson, William L.; Lapenta, William M.; Jedlovec, Gary J.; Peters-Lidard, Christa D.

2008-01-01

The exchange of energy and moisture between the Earth's surface and the atmospheric boundary layer plays a critical role in many meteorological processes. High-resolution, accurate representations of surface properties such as sea-surface temperature (SST), soil temperature and moisture content, ground fluxes, and vegetation are necessary to better understand the Earth-atmosphere interactions and improve numerical predictions of sensible weather. The NASA Short-term Prediction Research and Transition (SPoRT) Center has been conducting separate studies to examine the impacts of high-resolution land-surface initialization data from the Goddard Space Flight Center Land Information System (LIS) on subsequent WRF forecasts, as well as the influence of initializing WRF with SST composites derived from the MODIS instrument. This current project addresses the combined impacts of using high-resolution lower boundary data over both land (LIS data) and water (MODIS SSTs) on the subsequent daily WRF forecasts over Florida during May 2004. For this experiment, the WRF model is configured to run on a nested domain with 9- km and 3-kin grid spacing, centered on the Florida peninsula and adjacent coastal waters of the Gulf of Mexico and Atlantic Ocean. A control configuration of WRF is established to take all initial condition data from the NCEP Eta model. Meanwhile, two WRF experimental runs are configured to use high-resolution initialization data from (1) LIS land-surface data only, and (2) a combination of LIS data and high-resolution MODIS SST composites. The experiment involves running 24-hour simulations of the control WRF configuration, the MS-initialized WRF, and the LIS+MODIS-initialized WRF daily for the entire month of May 2004. All atmospheric data for initial and boundary conditions for the Control, LIS, and LIS+MODIS runs come from the NCEP Eta model on a 40-km grid. Verification statistics are generated at land surface observation sites and buoys, and the impacts of the high-resolution lower boundary data on the development and evolution of mesoscale circulations such as sea and land breezes are examined, This paper will present the results of these WRF modeling experiments using LIS and MODIS lower boundary datasets over the Florida peninsula during May 2004.

Downscaling seasonal to centennial simulations on distributed computing infrastructures using WRF model. The WRF4G project

NASA Astrophysics Data System (ADS)

Cofino, A. S.; Fernández Quiruelas, V.; Blanco Real, J. C.; García Díez, M.; Fernández, J.

2013-12-01

Nowadays Grid Computing is powerful computational tool which is ready to be used for scientific community in different areas (such as biomedicine, astrophysics, climate, etc.). However, the use of this distributed computing infrastructures (DCI) is not yet common practice in climate research, and only a few teams and applications in this area take advantage of this infrastructure. Thus, the WRF4G project objective is to popularize the use of this technology in the atmospheric sciences area. In order to achieve this objective, one of the most used applications has been taken (WRF; a limited- area model, successor of the MM5 model), that has a user community formed by more than 8000 researchers worldwide. This community develop its research activity on different areas and could benefit from the advantages of Grid resources (case study simulations, regional hind-cast/forecast, sensitivity studies, etc.). The WRF model is used by many groups, in the climate research community, to carry on downscaling simulations. Therefore this community will also benefit. However, Grid infrastructures have some drawbacks for the execution of applications that make an intensive use of CPU and memory for a long period of time. This makes necessary to develop a specific framework (middleware). This middleware encapsulates the application and provides appropriate services for the monitoring and management of the simulations and the data. Thus,another objective of theWRF4G project consists on the development of a generic adaptation of WRF to DCIs. It should simplify the access to the DCIs for the researchers, and also to free them from the technical and computational aspects of the use of theses DCI. Finally, in order to demonstrate the ability of WRF4G solving actual scientific challenges with interest and relevance on the climate science (implying a high computational cost) we will shown results from different kind of downscaling experiments, like ERA-Interim re-analysis, CMIP5 models, or seasonal. WRF4G is been used to run WRF simulations which are contributing to the CORDEX initiative and others projects like SPECS and EUPORIAS. This work is been partially funded by the European Regional Development Fund (ERDF) and the Spanish National R&D Plan 2008-2011 (CGL2011-28864)

WRF Simulations of the 20-22 January 2007 Snow Events over Eastern Canada: Comparison with In-Situ and Satellite Observations

NASA Technical Reports Server (NTRS)

Shi, J. J.; Tao, W.-K.; Matsui, T.; Cifelli, R.; Huo, A.; Lang, S.; Tokay, A.; Peters-Lidard, C.; Jackson, G.; Rutledge, S.;

Examining Interior Grid Nudging Techniques Using Two-Way Nesting in the WRF Model for Regional Climate Modeling

EPA Science Inventory

This study evaluates interior nudging techniques using the Weather Research and Forecasting (WRF) model for regional climate modeling over the conterminous United States (CONUS) using a two-way nested configuration. NCEP–Department of Energy Atmospheric Model Intercomparison Pro...

The relationship between transient and persistent worsening renal function and mortality in patients with acute decompensated heart failure.

PubMed

Aronson, Doron; Burger, Andrew J

2010-07-01

Worsening renal function (WRF) is an ominous complication in patients with acute heart failure syndrome (AHFS). Few data are available with regard to the clinical implications of transient versus persistent WRF in this setting. We studied 467 patients with AHFS and creatinine measurements at baseline and on days 2, 5, 14, and 30. WRF (>/= 0.5 mg/dL increase in serum creatinine above baseline at any time point) was defined as persistent when serum creatinine remained >/= 0.5 mg/dL above baseline throughout day 30, and transient when creatinine levels subsequently decreased to < 0.5 mg/dL above baseline. WRF occurred in 115 patients, and was transient in 39 patients (33.9%). The 6-month mortality rates were 17.3%, 20.5%, and 46.1% in patients without WRF, transient WRF, and persistent WRF, respectively. In a multivariable Cox model, compared with patients with stable renal function, the adjusted hazard ratio for mortality was 0.8 (95% CI 0.4-1.7; P = .58) in patients with transient WRF and 3.2 (95% CI 2.1-5.0; P < .0001) in patients with persistent WRF. Transient WRF is frequent among patients with AHFS. Whereas persistent WRF portends increased mortality, transient WRF appears to be associated with a better outcome as compared with persistent renal failure. Copyright 2010 Elsevier Inc. All rights reserved.

A new chemistry option in WRF/Chem v. 3.4 for the simulation of direct and indirect aerosol effects using VBS: evaluation against IMPACT-EUCAARI data

NASA Astrophysics Data System (ADS)

Tuccella, P.; Curci, G.; Grell, G. A.; Visconti, G.; Crumeroylle, S.; Schwarzenboeck, A.; Mensah, A. A.

2015-02-01

A parameterization for secondary organic aerosol (SOA) production based on the volatility basis set (VBS) approach has been coupled with microphysics and radiative scheme in WRF/Chem model. The new chemistry option called "RACM/MADE/VBS" was evaluated on a cloud resolving scale against ground-based and aircraft measurements collected during the IMPACT-EUCAARI campaign, and complemented with satellite data from MODIS. The day-to-day variability and the diurnal cycle of ozone (O3) and nitrogen oxides (NOx) at the surface is captured by the model. Surface aerosol mass of sulphate (SO4), nitrate (NO3), ammonium (NH4), and organic matter (OM) is simulated with a correlation larger than 0.55. WRF/Chem captures the vertical profile of the aerosol mass in both the planetary boundary layer (PBL) and free troposphere (FT) as a function of the synoptic condition, but the model does not capture the full range of the measured concentrations. Predicted OM concentration is at the lower end of the observed mass. The bias may be attributable to the missing aqueous chemistry processes of organic compounds, the uncertainties in meteorological fields, the assumption on the deposition velocity of condensable organic vapours, and the uncertainties in the anthropogenic emissions of primary organic carbon. Aerosol particle number concentration (condensation nuclei, CN) is overestimated by a factor 1.4 and 1.7 within PBL and FT, respectively. Model bias is most likely attributable to the uncertainties of primary particle emissions (mostly in the PBL) and to the nucleation rate. The overestimation of simulated cloud condensation nuclei (CCN) is more contained with respect to that of CN. The CCN efficiency, which is a measure of the ability of aerosol particles to nucleate cloud droplets, is underestimated by a factor of 1.5 and 3.8 in the PBL and FT, respectively. The comparison with MODIS data shows that the model overestimates the aerosol optical thickness (AOT). The domain averages (for one day) are 0.38 ± 0.12 and 0.42 ± 0.10 for MODIS and WRF/Chem data, respectively. Cloud water path (CWP) is overestimated on average by a factor of 1.7, whereas modelled cloud optical thickness (COT) agrees with observations within 10%. In a sensitivity test where the SOA was not included, simulated CWP is reduced by 40%, and its distribution function shifts toward lower values with respect to the reference run with SOA. The sensitivity test exhibits also 10% more optically thin clouds (COT < 40) and an average COT roughly halved. Moreover, the run with SOA shows convective clouds with an enhanced content of liquid and frozen hydrometers, and stronger updrafts and downdrafts. Considering that the previous version of WRF/Chem coupled with a modal aerosol module predicted very low SOA content (SORGAM mechanism) the new proposed option may lead to a better characterization of aerosol-cloud feedbacks.

An intercomparison of GCM and RCM dynamical downscaling for characterizing the hydroclimatology of California and Nevada

NASA Astrophysics Data System (ADS)

Xu, Z.; Rhoades, A.; Johansen, H.; Ullrich, P. A.; Collins, W. D.

2017-12-01

Dynamical downscaling is widely used to properly characterize regional surface heterogeneities that shape the local hydroclimatology. However, the factors in dynamical downscaling, including the refinement of model horizontal resolution, large-scale forcing datasets and dynamical cores, have not been fully evaluated. Two cutting-edge global-to-regional downscaling methods are used to assess these, specifically the variable-resolution Community Earth System Model (VR-CESM) and the Weather Research & Forecasting (WRF) regional climate model, under different horizontal resolutions (28, 14, and 7 km). Two groups of WRF simulations are driven by either the NCEP reanalysis dataset (WRF_NCEP) or VR-CESM outputs (WRF_VRCESM) to evaluate the effects of the large-scale forcing datasets. The impacts of dynamical core are assessed by comparing the VR-CESM simulations to the coupled WRF_VRCESM simulations with the same physical parameterizations and similar grid domains. The simulated hydroclimatology (i.e., total precipitation, snow cover, snow water equivalent and surface temperature) are compared with the reference datasets. The large-scale forcing datasets are critical to the WRF simulations in more accurately simulating total precipitation, SWE and snow cover, but not surface temperature. Both the WRF and VR-CESM results highlight that no significant benefit is found in the simulated hydroclimatology by just increasing horizontal resolution refinement from 28 to 7 km. Simulated surface temperature is sensitive to the choice of dynamical core. WRF generally simulates higher temperatures than VR-CESM, alleviates the systematic cold bias of DJF temperatures over the California mountain region, but overestimates the JJA temperature in California's Central Valley.

Regional Precipitation Forecast with Atmospheric InfraRed Sounder (AIRS) Profile Assimilation

NASA Technical Reports Server (NTRS)

Chou, S.-H.; Zavodsky, B. T.; Jedloved, G. J.

2010-01-01

Advanced technology in hyperspectral sensors such as the Atmospheric InfraRed Sounder (AIRS; Aumann et al. 2003) on NASA's polar orbiting Aqua satellite retrieve higher vertical resolution thermodynamic profiles than their predecessors due to increased spectral resolution. Although these capabilities do not replace the robust vertical resolution provided by radiosondes, they can serve as a complement to radiosondes in both space and time. These retrieved soundings can have a significant impact on weather forecasts if properly assimilated into prediction models. Several recent studies have evaluated the performance of specific operational weather forecast models when AIRS data are included in the assimilation process. LeMarshall et al. (2006) concluded that AIRS radiances significantly improved 500 hPa anomaly correlations in medium-range forecasts of the Global Forecast System (GFS) model. McCarty et al. (2009) demonstrated similar forecast improvement in 0-48 hour forecasts in an offline version of the operational North American Mesoscale (NAM) model when AIRS radiances were assimilated at the regional scale. Reale et al. (2008) showed improvements to Northern Hemisphere 500 hPa height anomaly correlations in NASA's Goddard Earth Observing System Model, Version 5 (GEOS-5) global system with the inclusion of partly cloudy AIRS temperature profiles. Singh et al. (2008) assimilated AIRS temperature and moisture profiles into a regional modeling system for a study of a heavy rainfall event during the summer monsoon season in Mumbai, India. This paper describes an approach to assimilate AIRS temperature and moisture profiles into a regional configuration of the Advanced Research Weather Research and Forecasting (WRF-ARW) model using its three-dimensional variational (3DVAR) assimilation system (WRF-Var; Barker et al. 2004). Section 2 describes the AIRS instrument and how the quality indicators are used to intelligently select the highest-quality data for assimilation. Section 3 presents an overall precipitation improvement with AIRS assimilation during a 37-day case study period, and Section 4 focuses on a single case study to further investigate the meteorological impact of AIRS profiles on synoptic scale models. Finally, Section 5 provides a summary of the paper.

Coupling of Large Eddy Simulations with Meteorological Models to simulate Methane Leaks from Natural Gas Storage Facilities

NASA Astrophysics Data System (ADS)

Prasad, K.

2017-12-01

Atmospheric transport is usually performed with weather models, e.g., the Weather Research and Forecasting (WRF) model that employs a parameterized turbulence model and does not resolve the fine scale dynamics generated by the flow around buildings and features comprising a large city. The NIST Fire Dynamics Simulator (FDS) is a computational fluid dynamics model that utilizes large eddy simulation methods to model flow around buildings at length scales much smaller than is practical with models like WRF. FDS has the potential to evaluate the impact of complex topography on near-field dispersion and mixing that is difficult to simulate with a mesoscale atmospheric model. A methodology has been developed to couple the FDS model with WRF mesoscale transport models. The coupling is based on nudging the FDS flow field towards that computed by WRF, and is currently limited to one way coupling performed in an off-line mode. This approach allows the FDS model to operate as a sub-grid scale model with in a WRF simulation. To test and validate the coupled FDS - WRF model, the methane leak from the Aliso Canyon underground storage facility was simulated. Large eddy simulations were performed over the complex topography of various natural gas storage facilities including Aliso Canyon, Honor Rancho and MacDonald Island at 10 m horizontal and vertical resolution. The goal of these simulations included improving and validating transport models as well as testing leak hypotheses. Forward simulation results were compared with aircraft and tower based in-situ measurements as well as methane plumes observed using the NASA Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) and the next generation instrument AVIRIS-NG. Comparison of simulation results with measurement data demonstrate the capability of the coupled FDS-WRF models to accurately simulate the transport and dispersion of methane plumes over urban domains. Simulated integrated methane enhancements will be presented and compared with results obtained from spectrometer data to estimate the temporally evolving methane flux during the Aliso Canyon blowout.

High-resolution simulation of heatwave events in New York City

NASA Astrophysics Data System (ADS)

Ramamurthy, P.; Li, D.; Bou-Zeid, E.

2017-04-01

Heatwave intensity and frequency are predicted to increase in the coming years, and this will bear adverse consequences to the environmental well-being and the socio-economic fabric in urbanized areas. The hazardous combination of increased heat storage and reduced water retention capacities of the land surface make the urban areas warmer than the surrounding rural areas in what is commonly known as the urban heat island (UHI) effect. The primary motives of this study are to quantify the interaction of this city-scale UHI with synoptic-scale heatwave episodes and to analyze the factors that mediate this interaction. A modified version of the Weather Research and Forecasting model (WRF) is utilized to simulate two heatwave episodes in New York City. The land surface scheme in the default WRF model is modified to better represent the surface to atmosphere exchanges over urban areas. Our results indicate that during the heatwave episodes, the daily-averaged UHI in NYC increased by 1.5 K. Furthermore, most of this amplification occurs in the mid-afternoon period when the temperatures peak. Wind direction and urban-rural contrasts in available energy and moisture availability are found to have significant and systematic effects on the UHI, but wind speed plays a secondary role.

WRF-TMH: predicting transmembrane helix by fusing composition index and physicochemical properties of amino acids.

PubMed

Hayat, Maqsood; Khan, Asifullah

2013-05-01

Membrane protein is the prime constituent of a cell, which performs a role of mediator between intra and extracellular processes. The prediction of transmembrane (TM) helix and its topology provides essential information regarding the function and structure of membrane proteins. However, prediction of TM helix and its topology is a challenging issue in bioinformatics and computational biology due to experimental complexities and lack of its established structures. Therefore, the location and orientation of TM helix segments are predicted from topogenic sequences. In this regard, we propose WRF-TMH model for effectively predicting TM helix segments. In this model, information is extracted from membrane protein sequences using compositional index and physicochemical properties. The redundant and irrelevant features are eliminated through singular value decomposition. The selected features provided by these feature extraction strategies are then fused to develop a hybrid model. Weighted random forest is adopted as a classification approach. We have used two benchmark datasets including low and high-resolution datasets. tenfold cross validation is employed to assess the performance of WRF-TMH model at different levels including per protein, per segment, and per residue. The success rates of WRF-TMH model are quite promising and are the best reported so far on the same datasets. It is observed that WRF-TMH model might play a substantial role, and will provide essential information for further structural and functional studies on membrane proteins. The accompanied web predictor is accessible at http://111.68.99.218/WRF-TMH/ .

A Deep Machine Learning Algorithm to Optimize the Forecast of Atmospherics

NASA Astrophysics Data System (ADS)

Russell, A. M.; Alliss, R. J.; Felton, B. D.

Space-based applications from imaging to optical communications are significantly impacted by the atmosphere. Specifically, the occurrence of clouds and optical turbulence can determine whether a mission is a success or a failure. In the case of space-based imaging applications, clouds produce atmospheric transmission losses that can make it impossible for an electro-optical platform to image its target. Hence, accurate predictions of negative atmospheric effects are a high priority in order to facilitate the efficient scheduling of resources. This study seeks to revolutionize our understanding of and our ability to predict such atmospheric events through the mining of data from a high-resolution Numerical Weather Prediction (NWP) model. Specifically, output from the Weather Research and Forecasting (WRF) model is mined using a Random Forest (RF) ensemble classification and regression approach in order to improve the prediction of low cloud cover over the Haleakala summit of the Hawaiian island of Maui. RF techniques have a number of advantages including the ability to capture non-linear associations between the predictors (in this case physical variables from WRF such as temperature, relative humidity, wind speed and pressure) and the predictand (clouds), which becomes critical when dealing with the complex non-linear occurrence of clouds. In addition, RF techniques are capable of representing complex spatial-temporal dynamics to some extent. Input predictors to the WRF-based RF model are strategically selected based on expert knowledge and a series of sensitivity tests. Ultimately, three types of WRF predictors are chosen: local surface predictors, regional 3D moisture predictors and regional inversion predictors. A suite of RF experiments is performed using these predictors in order to evaluate the performance of the hybrid RF-WRF technique. The RF model is trained and tuned on approximately half of the input dataset and evaluated on the other half. The RF approach is validated using in-situ observations of clouds. All of the hybrid RF-WRF experiments demonstrated here significantly outperform the base WRF local low cloud cover forecasts in terms of the probability of detection and the overall bias. In particular, RF experiments that use only regional three-dimensional moisture predictors from the WRF model produce the highest accuracy when compared to RF experiments that use local surface predictors only or regional inversion predictors only. Furthermore, adding multiple types of WRF predictors and additional WRF predictors to the RF algorithm does not necessarily add more value in the resulting forecasts, indicating that it is better to have a small set of meaningful predictors than to have a vast set of indiscriminately-chosen predictors. This work also reveals that the WRF-based RF approach is highly sensitive to the time period over which the algorithm is trained and evaluated. Future work will focus on developing a similar WRF-based RF model for high cloud prediction and expanding the algorithm to two-dimensions horizontally.

Sensitivity of simulated Martian atmospheric temperature to prescribed dust opacity distribution: Comparison of model results with reconstructed data from Mars Exploration Rover missions

NASA Astrophysics Data System (ADS)

Natarajan, Murali; Dwyer Cianciolo, Alicia; Fairlie, T. Duncan; Richardson, Mark I.; McConnochie, Timothy H.

2015-11-01

We use the Mars Weather Research and Forecasting (MarsWRF) general circulation model to simulate the atmospheric structure corresponding to the landing location and time of the Mars Exploration Rovers (MER) Spirit (A) and Opportunity (B) in 2004. The multiscale capability of MarsWRF facilitates high-resolution nested model runs centered near the landing site of each of the rovers. Dust opacity distributions based on measurements by Thermal Emission Spectrometer (TES) aboard the Mars Global Surveyor spacecraft, and those from an old version of the Mars Climate Database (MCD v3.1 released in 2001) are used to study the sensitivity of the model temperature profile to variations in the dust prescription. The reconstructed entry, descent, and landing (EDL) data from the rover missions are used for comparisons. We show that the model using dust opacity from TES limb and nadir data for the year of MER EDL, Mars Year 26 (MY26), yields temperature profiles in closer agreement with the reconstructed data than the prelaunch EDL simulations and models using other dust opacity specifications. The temperature at 100 Pa from the model (MY26) and the reconstruction are within 5°K. These results highlight the role of vertical dust opacity distribution in determining the atmospheric thermal structure. Similar studies involving data from past missions and models will be useful in understanding the extent to which atmospheric variability is captured by the models and in developing realistic preflight characterization required for future lander missions to Mars.

Feasibility of Virtual Machine and Cloud Computing Technologies for High Performance Computing

DTIC Science & Technology

2014-05-01

Hat Enterprise Linux SaaS software as a service VM virtual machine vNUMA virtual non-uniform memory access WRF weather research and forecasting...previously mentioned in Chapter I Section B1 of this paper, which is used to run the weather research and forecasting ( WRF ) model in their experiments...against a VMware virtualization solution of WRF . The experiment consisted of running WRF in a standard configuration between the D-VTM and VMware while

The Impact of Microphysics on Intensity and Structure of Hurricanes and Mesoscale Convective Systems

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Shi, Jainn J.; Jou, Ben Jong-Dao; Lee, Wen-Chau; Lin, Pay-Liam; Chang, Mei-Yu

2007-01-01

During the past decade, both research and operational numerical weather prediction models, e.g. Weather Research and Forecast (WRF) model, have started using more complex microphysical schemes originally developed for high-resolution cloud resolving models (CRMs) with a 1-2 km or less horizontal resolutions. WRF is a next-generation mesoscale forecast model and assimilation system that has incorporated modern software framework, advanced dynamics, numeric and data assimilation techniques, a multiple moveable nesting capability, and improved physical packages. WRF model can be used for a wide range of applications, from idealized research to operational forecasting, with an emphasis on horizontal grid sizes in the range of 1-10 km. The current WRF includes several different microphysics options such as Purdue Lin et al. (1983), WSM 6-class and Thompson microphysics schemes. We have recently implemented three sophisticated cloud microphysics schemes into WRF. The cloud microphysics schemes have been extensively tested and applied for different mesoscale systems in different geographical locations. The performances of these schemes have been compared to those from other WRF microphysics options. We are performing sensitivity tests in using WRF to examine the impact of six different cloud microphysical schemes on precipitation processes associated hurricanes and mesoscale convective systems developed at different geographic locations [Oklahoma (IHOP), Louisiana (Hurricane Katrina), Canada (C3VP - snow events), Washington (fire storm), India (Monsoon), Taiwan (TiMREX - terrain)]. We will determine the microphysical schemes for good simulated convective systems in these geographic locations. We are also performing the inline tracer calculation to comprehend the physical processes (i.e., boundary layer and each quadrant in the boundary layer) related to the development and structure of hurricanes and mesoscale convective systems.

Effects of 4D-Var data assimilation using remote sensing precipitation products in a WRF over the complex Heihe River Basin

NASA Astrophysics Data System (ADS)

Pan, Xiaoduo; Li, Xin; Cheng, Guodong

2017-04-01

Traditionally, ground-based, in situ observations, remote sensing, and regional climate modeling, individually, cannot provide the high-quality precipitation data required for hydrological prediction, especially over complex terrain. Data assimilation techniques are often used to assimilate ground observations and remote sensing products into models for dynamic downscaling. In this study, the Weather Research and Forecasting (WRF) model was used to assimilate two satellite precipitation products (TRMM 3B42 and FY-2D) using the 4D-Var data assimilation method. The results show that the assimilation of remote sensing precipitation products can improve the initial WRF fields of humidity and temperature, thereby improving precipitation forecasting and decreasing the spin-up time. Hence, assimilating TRMM and FY-2D remote sensing precipitation products using WRF 4D-Var can be viewed as a positive step toward improving the accuracy and lead time of numerical weather prediction models, particularly for short-term weather forecasting. Future work is proposed to assimilate a suite of remote sensing data, e.g., the combination of precipitation and soil moisture data, into a WRF model to improve 7-8 day forecasts of precipitation and other atmospheric variables.

Biogenic isoprene emissions driven by regional weather predictions using different initialization methods: case studies during the SEAC4RS and DISCOVER-AQ airborne campaigns

NASA Astrophysics Data System (ADS)

Huang, Min; Carmichael, Gregory R.; Crawford, James H.; Wisthaler, Armin; Zhan, Xiwu; Hain, Christopher R.; Lee, Pius; Guenther, Alex B.

2017-08-01

Land and atmospheric initial conditions of the Weather Research and Forecasting (WRF) model are often interpolated from a different model output. We perform case studies during NASA's SEAC4RS and DISCOVER-AQ Houston airborne campaigns, demonstrating that using land initial conditions directly downscaled from a coarser resolution dataset led to significant positive biases in the coupled NASA-Unified WRF (NUWRF, version 7) surface and near-surface air temperature and planetary boundary layer height (PBLH) around the Missouri Ozarks and Houston, Texas, as well as poorly partitioned latent and sensible heat fluxes. Replacing land initial conditions with the output from a long-term offline Land Information System (LIS) simulation can effectively reduce the positive biases in NUWRF surface air temperature by ˜ 2 °C. We also show that the LIS land initialization can modify surface air temperature errors almost 10 times as effectively as applying a different atmospheric initialization method. The LIS-NUWRF-based isoprene emission calculations by the Model of Emissions of Gases and Aerosols from Nature (MEGAN, version 2.1) are at least 20 % lower than those computed using the coarser resolution data-initialized NUWRF run, and are closer to aircraft-observation-derived emissions. Higher resolution MEGAN calculations are prone to amplified discrepancies with aircraft-observation-derived emissions on small scales. This is possibly a result of some limitations of MEGAN's parameterization and uncertainty in its inputs on small scales, as well as the representation error and the neglect of horizontal transport in deriving emissions from aircraft data. This study emphasizes the importance of proper land initialization to the coupled atmospheric weather modeling and the follow-on emission modeling. We anticipate it to also be critical to accurately representing other processes included in air quality modeling and chemical data assimilation. Having more confidence in the weather inputs is also beneficial for determining and quantifying the other sources of uncertainties (e.g., parameterization, other input data) of the models that they drive.

FULLY COUPLED "ONLINE" CHEMISTRY WITHIN THE WRF MODEL

EPA Science Inventory

A fully coupled "online" Weather Research and Forecasting/Chemistry (WRF/Chem) model has been developed. The air quality component of the model is fully consistent with the meteorological component; both components use the same transport scheme (mass and scalar preserving), the s...

Improved meteorology from an updated WRF/CMAQ modeling system with MODIS vegetation and albedo

EPA Science Inventory

Realistic vegetation characteristics and phenology from the Moderate Resolution Imaging Spectroradiometer (MODIS) products improve the simulation for the meteorology and air quality modeling system WRF/CMAQ (Weather Research and Forecasting model and Community Multiscale Air Qual...

Investigation of the Representation of OLEs and Terrain Effects within the Coastal Zone in the EDMF Parameterization Scheme: An Airborne Doppler Wind Lidar Perspective

DTIC Science & Technology

2015-10-21

rolls) in preparation for modifying current EDMF expressions We also continued to investigate the sensitivity of the WRF and COAMPS model to modified...allow non-collinear models to interact. During the fourth year, the TODWL data was also utilized by both the WRF and COAMPS model to help characterize...includes the contribution from both corrective and shear driven rolls within SCM, COAMPS and WRF <.’u:^--<^y\\,i/uU

Building-Resolved CFD Simulations for Greenhouse Gas Transport and Dispersion over Washington DC / Baltimore

NASA Astrophysics Data System (ADS)

Prasad, K.; Lopez-Coto, I.; Ghosh, S.; Mueller, K.; Whetstone, J. R.

2015-12-01

The North-East Corridor project aims to use a top-down inversion methodology to quantify sources of Greenhouse Gas (GHG) emissions over urban domains such as Washington DC / Baltimore with high spatial and temporal resolution. Atmospheric transport of tracer gases from an emission source to a tower mounted receptor are usually conducted using the Weather Research and Forecasting (WRF) model. For such simulations, WRF employs a parameterized turbulence model and does not resolve the fine scale dynamics generated by the flow around buildings and communities comprising a large city. The NIST Fire Dynamics Simulator (FDS) is a computational fluid dynamics model that utilizes large eddy simulation methods to model flow around buildings at length scales much smaller than is practical with WRF. FDS has the potential to evaluate the impact of complex urban topography on near-field dispersion and mixing difficult to simulate with a mesoscale atmospheric model. Such capabilities may be important in determining urban GHG emissions using atmospheric measurements. A methodology has been developed to run FDS as a sub-grid scale model within a WRF simulation. The coupling is based on nudging the FDS flow field towards that computed by WRF, and is currently limited to one way coupling performed in an off-line mode. Using the coupled WRF / FDS model, NIST will investigate the effects of the urban canopy at horizontal resolutions of 10-20 m in a domain of 12 x 12 km. The coupled WRF-FDS simulations will be used to calculate the dispersion of tracer gases in the North-East Corridor and to evaluate the upwind areas that contribute to tower observations, referred to in the inversion community as influence functions. Results of this study will provide guidance regarding the importance of explicit simulations of urban atmospheric turbulence in obtaining accurate estimates of greenhouse gas emissions and transport.

Tubular damage and worsening renal function in chronic heart failure.

PubMed

Damman, Kevin; Masson, Serge; Hillege, Hans L; Voors, Adriaan A; van Veldhuisen, Dirk J; Rossignol, Patrick; Proietti, Gianni; Barbuzzi, Savino; Nicolosi, Gian Luigi; Tavazzi, Luigi; Maggioni, Aldo P; Latini, Roberto

2013-10-01

This study sought to investigate the relationship between tubular damage and worsening renal function (WRF) in chronic heart failure (HF) BACKGROUND: WRF is associated with poor outcome in chronic HF. It is unclear whether urinary tubular markers may identify patients at risk for WRF. In 2,011 patients with chronic HF, we evaluated the ability of urinary tubular markers (N-acetyl-beta-d-glucosaminidase (NAG), kidney injury molecule (KIM)-1, and neutrophil gelatinase-associated lipocalin (NGAL) to predict WRF. Finally, we assessed the prognostic importance of WRF. A total of 290 patients (14.4%) experienced WRF during follow-up, and WRF was a strong and independent predictor of all-cause mortality and HF hospitalizations (hazard ratio [HR]: 2.87; 95% CI: 2.40 to 3.43; p < 0.001). Patients with WRF had lower baseline glomerular filtration rate and higher KIM-1, NAG, and NGAL levels. In a multivariable-adjusted model, KIM-1 was the strongest independent predictor of WRF (HR: 1.23; 95% CI: 1.09 to 1.39 per log increase; p = 0.001). WRF was associated with strongly impaired outcome in patients with chronic HF. Increased level of urinary KIM-1 was the strongest independent predictor of WRF and could therefore be used to identify patients at risk for WRF and poor clinical outcome. (GISSI-HF-Effects of n-3 PUFA and Rosuvastatin on Mortality-Morbidity of Patients With Symptomatic CHF; NCT00336336). Copyright © 2013 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Analyzing the Effects of Horizontal Resolution on Long-Term Coupled WRF-CMAQ Simulations

EPA Science Inventory

The objective of this study is to determine the adequacy of using a relatively coarse horizontal resolution (i.e. 36 km) to simulate long-term trends of pollutant concentrations and radiation variables with the coupled WRF-CMAQ model. To this end, WRF-CMAQ simulations over the co...

Urinary levels of novel kidney biomarkers and risk of true worsening renal function and mortality in patients with acute heart failure.

PubMed

Sokolski, Mateusz; Zymliński, Robert; Biegus, Jan; Siwołowski, Paweł; Nawrocka-Millward, Sylwia; Todd, John; Yerramilli, Malli Rama; Estis, Joel; Jankowska, Ewa Anita; Banasiak, Waldemar; Ponikowski, Piotr

2017-06-01

Recent studies indicate the need to redefine worsening renal function (WRF) in acute heart failure (AHF), linking a rise in creatinine with clinical status to identify patients who develop 'true WRF'. We evaluated the usefulness of serial assessment of urinary levels of neutrophil gelatinase-associated lipocalin (uNGAL), kidney injury molecule-1 (uKIM-1), and cystatin C (uCysC) for prediction of 'true WRF'. In 132 patients with AHF, uNGAL, uKIM-1, and uCysC were measured using a highly sensitive immunoassay based on a single-molecule counting technology (Singulex, Alameda, CA, USA) at baseline, day 2, and day 3. Patients who developed WRF (a ≥0.3 mg/dL increase in serum creatinine or a >25% decrease in the estimated glomerular filtration rate from the baseline value) were differentiated into those 'true WRF' (presence of deterioration/no improvement in clinical status during hospitalization) vs. 'pseudo-WRF' (uneventful clinical course). 'True WRF' occurred in 13 (10%), 'pseudo-WRF' in 15 (11%), whereas the remaining 104 (79%) patients did not develop WRF. Patients with 'true WRF' were more often females, had higher levels of NT-proBNP, creatinine, and urea on admission, higher urine albumin to creatinine ratio at day 2, higher uNGAL at baseline, day 2, and day 3, and higher KIM-1 at day 2 (vs. pseudo-WRF vs. without WRF, all P < 0.05). Patients with pseudo-WRF did not differ from those without WRF. In the multivariable model, elevated uNGAL at all time points and uKIM-1 at day 2 remained independent predictors of 'true WRF'. Elevated levels of uNGAL and uKIM-1 may predict development of 'true WRF' in AHF. © 2017 The Authors. European Journal of Heart Failure © 2017 European Society of Cardiology.

Recent Advances in Modeling of the Atmospheric Boundary Layer and Land Surface in the Coupled WRF-CMAQ Model

EPA Science Inventory

Advances in the land surface model (LSM) and planetary boundary layer (PBL) components of the WRF-CMAQ coupled meteorology and air quality modeling system are described. The aim of these modifications was primarily to improve the modeling of ground level concentrations of trace c...

“ How Reliable is the Couple of WRF & VIC Models”

EPA Science Inventory

The ability of the fully coupling of Weather Research & Forecasting Model (WRF) and Variable Infiltration Capacity (VIC) model to produce hydrological and climate variables was evaluated. First, the VIC model was run by using observed meteorological data and calibrated in the Upp...

Projected Applications of a ``Climate in a Box'' Computing System at the NASA Short-term Prediction Research and Transition (SPoRT) Center

NASA Astrophysics Data System (ADS)

Jedlovec, G.; Molthan, A.; Zavodsky, B.; Case, J.; Lafontaine, F.

2010-12-01

The NASA Short-term Prediction Research and Transition (SPoRT) Center focuses on the transition of unique observations and research capabilities to the operational weather community, with a goal of improving short-term forecasts on a regional scale. Advances in research computing have lead to “Climate in a Box” systems, with hardware configurations capable of producing high resolution, near real-time weather forecasts, but with footprints, power, and cooling requirements that are comparable to desktop systems. The SPoRT Center has developed several capabilities for incorporating unique NASA research capabilities and observations with real-time weather forecasts. Planned utilization includes the development of a fully-cycled data assimilation system used to drive 36-48 hour forecasts produced by the NASA Unified version of the Weather Research and Forecasting (WRF) model (NU-WRF). The horsepower provided by the “Climate in a Box” system is expected to facilitate the assimilation of vertical profiles of temperature and moisture provided by the Atmospheric Infrared Sounder (AIRS) aboard the NASA Aqua satellite. In addition, the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard NASA’s Aqua and Terra satellites provide high-resolution sea surface temperatures and vegetation characteristics. The development of MODIS normalized difference vegetation index (NVDI) composites for use within the NASA Land Information System (LIS) will assist in the characterization of vegetation, and subsequently the surface albedo and processes related to soil moisture. Through application of satellite simulators, NASA satellite instruments can be used to examine forecast model errors in cloud cover and other characteristics. Through the aforementioned application of the “Climate in a Box” system and NU-WRF capabilities, an end goal is the establishment of a real-time forecast system that fully integrates modeling and analysis capabilities developed within the NASA SPoRT Center, with benefits provided to the operational forecasting community.

Projected Applications of a "Climate in a Box" Computing System at the NASA Short-Term Prediction Research and Transition (SPoRT) Center

NASA Technical Reports Server (NTRS)

Jedlovec, Gary J.; Molthan, Andrew L.; Zavodsky, Bradley; Case, Jonathan L.; LaFontaine, Frank J.

2010-01-01

The NASA Short-term Prediction Research and Transition (SPoRT) Center focuses on the transition of unique observations and research capabilities to the operational weather community, with a goal of improving short-term forecasts on a regional scale. Advances in research computing have lead to "Climate in a Box" systems, with hardware configurations capable of producing high resolution, near real-time weather forecasts, but with footprints, power, and cooling requirements that are comparable to desktop systems. The SPoRT Center has developed several capabilities for incorporating unique NASA research capabilities and observations with real-time weather forecasts. Planned utilization includes the development of a fully-cycled data assimilation system used to drive 36-48 hour forecasts produced by the NASA Unified version of the Weather Research and Forecasting (WRF) model (NU-WRF). The horsepower provided by the "Climate in a Box" system is expected to facilitate the assimilation of vertical profiles of temperature and moisture provided by the Atmospheric Infrared Sounder (AIRS) aboard the NASA Aqua satellite. In addition, the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard NASA s Aqua and Terra satellites provide high-resolution sea surface temperatures and vegetation characteristics. The development of MODIS normalized difference vegetation index (NVDI) composites for use within the NASA Land Information System (LIS) will assist in the characterization of vegetation, and subsequently the surface albedo and processes related to soil moisture. Through application of satellite simulators, NASA satellite instruments can be used to examine forecast model errors in cloud cover and other characteristics. Through the aforementioned application of the "Climate in a Box" system and NU-WRF capabilities, an end goal is the establishment of a real-time forecast system that fully integrates modeling and analysis capabilities developed within the NASA SPoRT Center, with benefits provided to the operational forecasting community.

The sensitivity of WRF daily summertime simulations over West Africa to alternative parameterizations. Part 2: Precipitation.

PubMed

Noble, Erik; Druyan, Leonard M; Fulakeza, Matthew

2016-01-01

This paper evaluates the performance of the Weather and Research Forecasting (WRF) model as a regional-atmospheric model over West Africa. It tests WRF sensitivity to 64 configurations of alternative parameterizations in a series of 104 twelve-day September simulations during eleven consecutive years, 2000-2010. The 64 configurations combine WRF parameterizations of cumulus convection, radiation, surface-hydrology, and PBL. Simulated daily and total precipitation results are validated against Global Precipitation Climatology Project (GPCP) and Tropical Rainfall Measuring Mission (TRMM) data. Particular attention is given to westward-propagating precipitation maxima associated with African Easterly Waves (AEWs). A wide range of daily precipitation validation scores demonstrates the influence of alternative parameterizations. The best WRF performers achieve time-longitude correlations (against GPCP) of between 0.35-0.42 and spatiotemporal variability amplitudes only slightly higher than observed estimates. A parallel simulation by the benchmark Regional Model-v.3 achieves a higher correlation (0.52) and realistic spatiotemporal variability amplitudes. The largest favorable impact on WRF precipitation validation is achieved by selecting the Grell-Devenyi convection scheme, resulting in higher correlations against observations than using the Kain-Fritch convection scheme. Other parameterizations have less obvious impact. Validation statistics for optimized WRF configurations simulating the parallel period during 2000-2010 are more favorable for 2005, 2006, and 2008 than for other years. The selection of some of the same WRF configurations as high scorers in both circulation and precipitation validations supports the notion that simulations of West African daily precipitation benefit from skillful simulations of associated AEW vorticity centers and that simulations of AEWs would benefit from skillful simulations of convective precipitation.

The sensitivity of WRF daily summertime simulations over West Africa to alternative parameterizations. Part 2: Precipitation

PubMed Central

Noble, Erik; Druyan, Leonard M.; Fulakeza, Matthew

2018-01-01

This paper evaluates the performance of the Weather and Research Forecasting (WRF) model as a regional-atmospheric model over West Africa. It tests WRF sensitivity to 64 configurations of alternative parameterizations in a series of 104 twelve-day September simulations during eleven consecutive years, 2000–2010. The 64 configurations combine WRF parameterizations of cumulus convection, radiation, surface-hydrology, and PBL. Simulated daily and total precipitation results are validated against Global Precipitation Climatology Project (GPCP) and Tropical Rainfall Measuring Mission (TRMM) data. Particular attention is given to westward-propagating precipitation maxima associated with African Easterly Waves (AEWs). A wide range of daily precipitation validation scores demonstrates the influence of alternative parameterizations. The best WRF performers achieve time-longitude correlations (against GPCP) of between 0.35–0.42 and spatiotemporal variability amplitudes only slightly higher than observed estimates. A parallel simulation by the benchmark Regional Model-v.3 achieves a higher correlation (0.52) and realistic spatiotemporal variability amplitudes. The largest favorable impact on WRF precipitation validation is achieved by selecting the Grell-Devenyi convection scheme, resulting in higher correlations against observations than using the Kain-Fritch convection scheme. Other parameterizations have less obvious impact. Validation statistics for optimized WRF configurations simulating the parallel period during 2000–2010 are more favorable for 2005, 2006, and 2008 than for other years. The selection of some of the same WRF configurations as high scorers in both circulation and precipitation validations supports the notion that simulations of West African daily precipitation benefit from skillful simulations of associated AEW vorticity centers and that simulations of AEWs would benefit from skillful simulations of convective precipitation. PMID:29563651

Regional modelling of polycyclic aromatic hydrocarbons: WRF-Chem-PAH model development and East Asia case studies

NASA Astrophysics Data System (ADS)

Mu, Qing; Lammel, Gerhard; Gencarelli, Christian N.; Hedgecock, Ian M.; Chen, Ying; Přibylová, Petra; Teich, Monique; Zhang, Yuxuan; Zheng, Guangjie; van Pinxteren, Dominik; Zhang, Qiang; Herrmann, Hartmut; Shiraiwa, Manabu; Spichtinger, Peter; Su, Hang; Pöschl, Ulrich; Cheng, Yafang

2017-10-01

Polycyclic aromatic hydrocarbons (PAHs) are hazardous pollutants, with increasing emissions in pace with economic development in East Asia, but their distribution and fate in the atmosphere are not yet well understood. We extended the regional atmospheric chemistry model WRF-Chem (Weather Research Forecast model with Chemistry module) to comprehensively study the atmospheric distribution and the fate of low-concentration, slowly degrading semivolatile compounds. The WRF-Chem-PAH model reflects the state-of-the-art understanding of current PAHs studies with several new or updated features. It was applied for PAHs covering a wide range of volatility and hydrophobicity, i.e. phenanthrene, chrysene and benzo[a]pyrene, in East Asia. Temporally highly resolved PAH concentrations and particulate mass fractions were evaluated against observations. The WRF-Chem-PAH model is able to reasonably well simulate the concentration levels and particulate mass fractions of PAHs near the sources and at a remote outflow region of East Asia, in high spatial and temporal resolutions. Sensitivity study shows that the heterogeneous reaction with ozone and the homogeneous reaction with the nitrate radical significantly influence the fate and distributions of PAHs. The methods to implement new species and to correct the transport problems can be applied to other newly implemented species in WRF-Chem.

Using Virtualization to Integrate Weather, Climate, and Coastal Science Education

NASA Astrophysics Data System (ADS)

Davis, J. R.; Paramygin, V. A.; Figueiredo, R.; Sheng, Y.

2012-12-01

To better understand and communicate the important roles of weather and climate on the coastal environment, a unique publically available tool is being developed to support research, education, and outreach activities. This tool uses virtualization technologies to facilitate an interactive, hands-on environment in which students, researchers, and general public can perform their own numerical modeling experiments. While prior efforts have focused solely on the study of the coastal and estuary environments, this effort incorporates the community supported weather and climate model (WRF-ARW) into the Coastal Science Educational Virtual Appliance (CSEVA), an education tool used to assist in the learning of coastal transport processes; storm surge and inundation; and evacuation modeling. The Weather Research and Forecasting (WRF) Model is a next-generation, community developed and supported, mesoscale numerical weather prediction system designed to be used internationally for research, operations, and teaching. It includes two dynamical solvers (ARW - Advanced Research WRF and NMM - Nonhydrostatic Mesoscale Model) as well as a data assimilation system. WRF-ARW is the ARW dynamics solver combined with other components of the WRF system which was developed primarily at NCAR, community support provided by the Mesoscale and Microscale Meteorology (MMM) division of National Center for Atmospheric Research (NCAR). Included with WRF is the WRF Pre-processing System (WPS) which is a set of programs to prepare input for real-data simulations. The CSEVA is based on the Grid Appliance (GA) framework and is built using virtual machine (VM) and virtual networking technologies. Virtualization supports integration of an operating system, libraries (e.g. Fortran, C, Perl, NetCDF, etc. necessary to build WRF), web server, numerical models/grids/inputs, pre-/post-processing tools (e.g. WPS / RIP4 or UPS), graphical user interfaces, "Cloud"-computing infrastructure and other tools into a single ready-to-use package. Thus, the previous ornery task of setting up and compiling these tools becomes obsolete and the research, educator or student can focus on using the tools to study the interactions between weather, climate and the coastal environment. The incorporation of WRF into the CSEVA has been designed to be synergistic with the extensive online tutorials and biannual tutorials hosted by NCAR. Included are working examples of the idealized test simulations provided with WRF (2D sea breeze and squalls, a large eddy simulation, a Held and Suarez simulation, etc.) To demonstrate the integration of weather, coastal and coastal science education, example applications are being developed to demonstrate how the system can be used to couple a coastal and estuarine circulation, transport and storm surge model with downscale reanalysis weather and future climate predictions. Documentation, tutorials and the enhanced CSEVA itself will be found on the web at: http://cseva.coastal.ufl.edu.

Implementation of a gust front head collapse scheme in the WRF numerical model

NASA Astrophysics Data System (ADS)

Lompar, Miloš; Ćurić, Mladjen; Romanic, Djordje

2018-05-01

Gust fronts are thunderstorm-related phenomena usually associated with severe winds which are of great importance in theoretical meteorology, weather forecasting, cloud dynamics and precipitation, and wind engineering. An important feature of gust fronts demonstrated through both theoretical and observational studies is the periodic collapse and rebuild of the gust front head. This cyclic behavior of gust fronts results in periodic forcing of vertical velocity ahead of the parent thunderstorm, which consequently influences the storm dynamics and microphysics. This paper introduces the first gust front pulsation parameterization scheme in the WRF-ARW model (Weather Research and Forecasting-Advanced Research WRF). The influence of this new scheme on model performances is tested through investigation of the characteristics of an idealized supercell cumulonimbus cloud, as well as studying a real case of thunderstorms above the United Arab Emirates. In the ideal case, WRF with the gust front scheme produced more precipitation and showed different time evolution of mixing ratios of cloud water and rain, whereas the mixing ratios of ice and graupel are almost unchanged when compared to the default WRF run without the parameterization of gust front pulsation. The included parameterization did not disturb the general characteristics of thunderstorm cloud, such as the location of updraft and downdrafts, and the overall shape of the cloud. New cloud cells in front of the parent thunderstorm are also evident in both ideal and real cases due to the included forcing of vertical velocity caused by the periodic collapse of the gust front head. Despite some differences between the two WRF simulations and satellite observations, the inclusion of the gust front parameterization scheme produced more cumuliform clouds and seem to match better with real observations. Both WRF simulations gave poor results when it comes to matching the maximum composite radar reflectivity from radar measurement. Similar to the ideal case, WRF model with the gust front scheme gave more precipitation than the default WRF run. In particular, the gust front scheme increased the area characterized with light precipitation and diminished the development of very localized and intense precipitation.

Evaluation of WRF Parameterizations for Air Quality Applications over the Midwest USA

NASA Astrophysics Data System (ADS)

Zheng, Z.; Fu, K.; Balasubramanian, S.; Koloutsou-Vakakis, S.; McFarland, D. M.; Rood, M. J.

2017-12-01

Reliable predictions from Chemical Transport Models (CTMs) for air quality research require accurate gridded weather inputs. In this study, a sensitivity analysis of 17 Weather Research and Forecast (WRF) model runs was conducted to explore the optimum configuration in six physics categories (i.e., cumulus, surface layer, microphysics, land surface model, planetary boundary layer, and longwave/shortwave radiation) for the Midwest USA. WRF runs were initally conducted over four days in May 2011 for a 12 km x 12 km domain over contiguous USA and a nested 4 km x 4 km domain over the Midwest USA (i.e., Illinois and adjacent areas including Iowa, Indiana, and Missouri). Model outputs were evaluated statistically by comparison with meteorological observations (DS337.0, METAR data, and the Water and Atmospheric Resources Monitoring Network) and resulting statistics were compared to benchmark values from the literature. Identified optimum configurations of physics parametrizations were then evaluated for the whole months of May and October 2011 to evaluate WRF model performance for Midwestern spring and fall seasons. This study demonstrated that for the chosen physics options, WRF predicted well temperature (Index of Agreement (IOA) = 0.99), pressure (IOA = 0.99), relative humidity (IOA = 0.93), wind speed (IOA = 0.85), and wind direction (IOA = 0.97). However, WRF did not predict daily precipitation satisfactorily (IOA = 0.16). Developed gridded weather fields will be used as inputs to a CTM ensemble consisting of the Comprehensive Air Quality Model with Extensions to study impacts of chemical fertilizer usage on regional air quality in the Midwest USA.

Modeling regional air quality and climate: improving organic aerosol and aerosol activation processes in WRF/Chem version 3.7.1

NASA Astrophysics Data System (ADS)

Yahya, Khairunnisa; Glotfelty, Timothy; Wang, Kai; Zhang, Yang; Nenes, Athanasios

2017-06-01

Air quality and climate influence each other through the uncertain processes of aerosol formation and cloud droplet activation. In this study, both processes are improved in the Weather, Research and Forecasting model with Chemistry (WRF/Chem) version 3.7.1. The existing Volatility Basis Set (VBS) treatments for organic aerosol (OA) formation in WRF/Chem are improved by considering the following: the secondary OA (SOA) formation from semi-volatile primary organic aerosol (POA), a semi-empirical formulation for the enthalpy of vaporization of SOA, and functionalization and fragmentation reactions for multiple generations of products from the oxidation of VOCs. Over the continental US, 2-month-long simulations (May to June 2010) are conducted and results are evaluated against surface and aircraft observations during the Nexus of Air Quality and Climate Change (CalNex) campaign. Among all the configurations considered, the best performance is found for the simulation with the 2005 Carbon Bond mechanism (CB05) and the VBS SOA module with semivolatile POA treatment, 25 % fragmentation, and the emissions of semi-volatile and intermediate volatile organic compounds being 3 times the original POA emissions. Among the three gas-phase mechanisms (CB05, CB6, and SAPRC07) used, CB05 gives the best performance for surface ozone and PM2. 5 concentrations. Differences in SOA predictions are larger for the simulations with different VBS treatments (e.g., nonvolatile POA versus semivolatile POA) compared to the simulations with different gas-phase mechanisms. Compared to the simulation with CB05 and the default SOA module, the simulations with the VBS treatment improve cloud droplet number concentration (CDNC) predictions (normalized mean biases from -40.8 % to a range of -34.6 to -27.7 %), with large differences between CB05-CB6 and SAPRC07 due to large differences in their OH and HO2 predictions. An advanced aerosol activation parameterization based on the Fountoukis and Nenes (2005) series reduces the large negative CDNC bias associated with the default Abdul Razzak and Ghan (2000) parameterization from -35.4 % to a range of -0.8 to 7.1 %. However, it increases the errors due to overpredictions of CDNC, mainly over the northeastern US. This work indicates a need to improve other aerosol-cloud-radiation processes in the model, such as the spatial distribution of aerosol optical depth and cloud condensation nuclei, in order to further improve CDNC predictions.

Diagnostic Analysis of Ozone Concentrations Simulated by Two Regional-Scale Air Quality Models

EPA Science Inventory

Since the Community Multiscale Air Quality modeling system (CMAQ) and the Weather Research and Forecasting with Chemistry model (WRF/Chem) use different approaches to simulate the interaction of meteorology and chemistry, this study compares the CMAQ and WRF/Chem air quality simu...

WRF nested large-eddy simulations of deep convection during SEAC4RS

NASA Astrophysics Data System (ADS)

Heath, Nicholas Kyle

Deep convection is an important component of atmospheric circulations that affects many aspects of weather and climate. Therefore, improved understanding and realistic simulations of deep convection are critical to both operational and climate forecasts. Large-eddy simulations (LESs) often are used with observations to enhance understanding of convective processes. This study develops and evaluates a nested-LES method using the Weather Research and Forecasting (WRF) model. Our goal is to evaluate the extent to which the WRF nested-LES approach is useful for studying deep convection during a real-world case. The method was applied on 2 September 2013, a day of continental convection having a robust set of ground and airborne data available for evaluation. A three domain mesoscale WRF simulation is run first. Then, the finest mesoscale output (1.35 km grid length) is used to separately drive nested-LES domains with grid lengths of 450 and 150 m. Results reveal that the nested-LES approach reasonably simulates a broad spectrum of observations, from reflectivity distributions to vertical velocity profiles, during the study period. However, reducing the grid spacing does not necessarily improve results for our case, with the 450 m simulation outperforming the 150 m version. We find that simulated updrafts in the 150 m simulation are too narrow to overcome the negative effects of entrainment, thereby generating convection that is weaker than observed. Increasing the sub-grid mixing length in the 150 m simulation leads to deeper, more realistic convection, but comes at the expense of delaying the onset of the convection. Overall, results show that both the 450 m and 150 m simulations are influenced considerably by the choice of sub-grid mixing length used in the LES turbulence closure. Finally, the simulations and observations are used to study the processes forcing strong midlevel cloud-edge downdrafts that were observed on 2 September. Results suggest that these downdrafts are forced by evaporative cooling due to mixing near cloud edge and by vertical perturbation pressure gradient forces acting to restore mass continuity around neighboring updrafts. We conclude that the WRF nested-LES approach provides an effective method for studying deep convection for our real-world case. The method can be used to provide insight into physical processes that are important to understanding observations. The WRF nested-LES approach could be adapted for other case studies in which high-resolution observations are available for validation.

Precipitation intercomparison of a set of satellite- and raingauge-derived datasets, ERA Interim reanalysis, and a single WRF regional climate simulation over Europe and the North Atlantic

NASA Astrophysics Data System (ADS)

Skok, Gregor; Žagar, Nedjeljka; Honzak, Luka; Žabkar, Rahela; Rakovec, Jože; Ceglar, Andrej

2016-01-01

The study presents a precipitation intercomparison based on two satellite-derived datasets (TRMM 3B42, CMORPH), four raingauge-based datasets (GPCC, E-OBS, Willmott & Matsuura, CRU), ERA Interim reanalysis (ERAInt), and a single climate simulation using the WRF model. The comparison was performed for a domain encompassing parts of Europe and the North Atlantic over the 11-year period of 2000-2010. The four raingauge-based datasets are similar to the TRMM dataset with biases over Europe ranging from -7 % to +4 %. The spread among the raingauge-based datasets is relatively small over most of Europe, although areas with greater uncertainty (more than 30 %) exist, especially near the Alps and other mountainous regions. There are distinct differences between the datasets over the European land area and the Atlantic Ocean in comparison to the TRMM dataset. ERAInt has a small dry bias over the land; the WRF simulation has a large wet bias (+30 %), whereas CMORPH is characterized by a large and spatially consistent dry bias (-21 %). Over the ocean, both ERAInt and CMORPH have a small wet bias (+8 %) while the wet bias in WRF is significantly larger (+47 %). ERAInt has the highest frequency of low-intensity precipitation while the frequency of high-intensity precipitation is the lowest due to its lower native resolution. Both satellite-derived datasets have more low-intensity precipitation over the ocean than over the land, while the frequency of higher-intensity precipitation is similar or larger over the land. This result is likely related to orography, which triggers more intense convective precipitation, while the Atlantic Ocean is characterized by more homogenous large-scale precipitation systems which are associated with larger areas of lower intensity precipitation. However, this is not observed in ERAInt and WRF, indicating the insufficient representation of convective processes in the models. Finally, the Fraction Skill Score confirmed that both models perform better over the Atlantic Ocean with ERAInt outperforming the WRF at low thresholds and WRF outperforming ERAInt at higher thresholds. The diurnal cycle is simulated better in the WRF simulation than in ERAInt, although WRF could not reproduce well the amplitude of the diurnal cycle. While the evaluation of the WRF model confirms earlier findings related to the model's wet bias over European land, the applied satellite-derived precipitation datasets revealed differences between the land and ocean areas along with uncertainties in the observation datasets.

Analysis and High-Resolution Modeling of Tropical Cyclogenesis During the TCS-08 and TPARC Field Campaign

DTIC Science & Technology

2014-10-13

synoptic and dynamic aspects of cyclogenesis, a multi-nested WRF model (with 2 km resolution in the innermost mesh) will be used to simulate both...intraseasonal and interannual variability of TC activity in the WNP. For the data assimilation task, WRF 3DVar assimilation system will be employed...simulated using WRF . This genesis is associated with Rossby wave energy dispersion of a pre- existing TC Bills (2000). Using the reanalysis data as an

Change in Weather Research and Forecasting (WRF) Model Accuracy with Age of Input Data from the Global Forecast System (GFS)

DTIC Science & Technology

2016-09-01

Laboratory Change in Weather Research and Forecasting (WRF) Model Accuracy with Age of Input Data from the Global Forecast System (GFS) by JL Cogan...analysis. As expected, accuracy generally tended to decline as the large-scale data aged , but appeared to improve slightly as the age of the large...19 Table 7 Minimum and maximum mean RMDs for each WRF time (or GFS data age ) category. Minimum and

Retrospective Snow Analysis Across the Continental United States for the National Water Model

NASA Astrophysics Data System (ADS)

Karsten, L. R.; Gochis, D.; Dugger, A. L.; McCreight, J. L.; Barlage, M. J.; Fall, G. M.; Olheiser, C.

2016-12-01

For large portions of the United States, snow plays a vital role in hydrologic prediction. This is particularly true in the mountain west where snowmelt contributes up to 80% of total streamflow runoff. The Office of Water Prediction (OWP) will begin running the National Water Model (NWM) during the second half of 2016, which is a continental-scale implementation of the WRF-Hydro community hydrologic modeling framework. Assessing and benchmarking the performance of the snow component of the NWM is important for future research-to-operations activities and for forecasters to better understand NWM output. For this study, WRF-Hydro was ran using the same domain and physics options as the NWM (1 km LSM, 250m overland routing, and NHDPlus Version 2.1 channel network). The land surface component chosen is Noah-MP land surface model. Forcing from the National Land Data Assimilation System (NLDAS-2) was downscaled from the native 0.125 degree resolution to the 1 km modeling domain to drive the model. The model was ran over a 5-year retrospective period to gauge multi-year performance of the snow states. Output was analyzed against both in-situ observations, such as SNOTEL, and the Snow Data Assimilation System (SNODAS). In addition, gridded snow states and SNODAS grids were aggregated to Omernik-derived ecological regions. This was done in order to break up snow analysis by regions that share similar ecological and physiographic characteristics. Results show WRF-Hydro is able to capture peak timing across most of the mountain west fairly well. In terms of magnitudes, the model struggles across portions of the west with a low bias. This is especially true in the Cascades, which could be traced back to precipitation partitioning issues in the model. Across the central Rockies, the model exhibits a lower dry bias showing improved performance there. Previous literature suggests a dry bias in the precipitation out west may be contributing to model performance. East of the Rockies, the model captures events well, including both timing and magnitude when compared to SNODAS. There are issues with particular events in these regions, but this may be due to the nature of the events being mixed-phase. Overall performance with snow simulation for the NWM shows promise for use in operations.

Evaluating Aerosol Process Modules within the Framework of the Aerosol Modeling Testbed

NASA Astrophysics Data System (ADS)

Fast, J. D.; Velu, V.; Gustafson, W. I.; Chapman, E.; Easter, R. C.; Shrivastava, M.; Singh, B.

2012-12-01

Factors that influence predictions of aerosol direct and indirect forcing, such as aerosol mass, composition, size distribution, hygroscopicity, and optical properties, still contain large uncertainties in both regional and global models. New aerosol treatments are usually implemented into a 3-D atmospheric model and evaluated using a limited number of measurements from a specific case study. Under this modeling paradigm, the performance and computational efficiency of several treatments for a specific aerosol process cannot be adequately quantified because many other processes among various modeling studies (e.g. grid configuration, meteorology, emission rates) are different as well. The scientific community needs to know the advantages and disadvantages of specific aerosol treatments when the meteorology, chemistry, and other aerosol processes are identical in order to reduce the uncertainties associated with aerosols predictions. To address these issues, an Aerosol Modeling Testbed (AMT) has been developed that systematically and objectively evaluates new aerosol treatments for use in regional and global models. The AMT consists of the modular Weather Research and Forecasting (WRF) model, a series testbed cases for which extensive in situ and remote sensing measurements of meteorological, trace gas, and aerosol properties are available, and a suite of tools to evaluate the performance of meteorological, chemical, aerosol process modules. WRF contains various parameterizations of meteorological, chemical, and aerosol processes and includes interactive aerosol-cloud-radiation treatments similar to those employed by climate models. In addition, the physics suite from the Community Atmosphere Model version 5 (CAM5) have also been ported to WRF so that they can be tested at various spatial scales and compared directly with field campaign data and other parameterizations commonly used by the mesoscale modeling community. Data from several campaigns, including the 2006 MILAGRO, 2008 ISDAC, 2008 VOCALS, 2010 CARES, and 2010 CalNex campaigns, have been incorporated into the AMT as testbed cases. Data from operational networks (e.g. air quality, meteorology, satellite) are also included in the testbed cases to supplement the field campaign data. The CARES and CalNex testbed cases are used to demonstrate how the AMT can be used to assess the strengths and weaknesses of simple and complex representations of aerosol processes in relation to computational cost. Anticipated enhancements to the AMT and how this type of testbed can be used by the scientific community to foster collaborations and coordinate aerosol modeling research will also be discussed.

Assessment of the effects of horizontal grid resolution on long-term air quality trends using coupled WRF-CMAQ simulations

EPA Science Inventory

The objective of this study is to determine the adequacy of using a relatively coarse horizontal resolution (i.e. 36 km) to simulate long-term trends of pollutant concentrations and radiation variables with the coupled WRF-CMAQ model. WRF-CMAQ simulations over the continental Uni...

Sensitivity of an Integrated Mesoscale Atmosphere and Agriculture Land Modeling System (WRF/CMAQ-EPIC) to MODIS Vegetation and Lightning Assimilation

EPA Science Inventory

The combined meteorology and air quality modeling system composed of the Weather Research and Forecast (WRF) model and Community Multiscale Air Quality (CMAQ) model is an important decision support tool that is used in research and regulatory decisions related to emissions, meteo...

How reliable is the offline linkage of Weather Research & Forecasting Model (WRF) and Variable Infiltration Capacity (VIC) model?

EPA Science Inventory

The aim for this research is to evaluate the ability of the offline linkage of Weather Research & Forecasting Model (WRF) and Variable Infiltration Capacity (VIC) model to produce hydrological, e.g. evaporation (ET), soil moisture (SM), runoff, and baseflow. First, the VIC mo...

High-resolution dynamical downscaling of the future Alpine climate

NASA Astrophysics Data System (ADS)

Bozhinova, Denica; José Gómez-Navarro, Juan; Raible, Christoph

2017-04-01

The Alpine region and Switzerland is a challenging area for simulating and analysing Global Climate Model (GCM) results. This is mostly due to the combination of a very complex topography and the still rather coarse horizontal resolution of current GCMs, in which not all of the many-scale processes that drive the local weather and climate can be resolved. In our study, the Weather Research and Forecasting (WRF) model is used to dynamically downscale a GCM simulation to a resolution as high as 2 km x 2 km. WRF is driven by initial and boundary conditions produced with the Community Earth System Model (CESM) for the recent past (control run) and until 2100 using the RCP8.5 climate scenario (future run). The control run downscaled with WRF covers the period 1976-2005, while the future run investigates a 20-year-slice simulated for the 2080-2099. We compare the control WRF-CESM simulations to an observational product provided by MeteoSwiss and an additional WRF simulation driven by the ERA-Interim reanalysis, to estimate the bias that is introduced by the extra modelling step of our framework. Several bias-correction methods are evaluated, including a quantile mapping technique, to ameliorate the bias in the control WRF-CESM simulation. In the next step of our study these corrections are applied to our future WRF-CESM run. The resulting downscaled and bias-corrected data is analysed for the properties of precipitation and wind speed in the future climate. Our special interest focuses on the absolute quantities simulated for these meteorological variables as these are used to identify extreme events, such as wind storms and situations that can lead to floods.

Effects of soot-induced snow albedo change on snowpack and hydrological cycle in western U.S. based on WRF chemistry and regional climate simulations

NASA Astrophysics Data System (ADS)

Qian, Y.; Gustafson, W. I.; Leung, R.; Ghan, S. J.

2008-12-01

Radiative forcing induced by soot on snow is an important anthropogenic forcing affecting the global climate. In this study we simulated the deposition of soot aerosol on snow and the resulting impact on snowpack and the hydrological cycle in the western United States. A yearlong simulation was performed using the chemistry version of the Weather Research and Forecasting model (WRF-Chem) to determine the soot deposition, followed by three simulations using WRF in meteorology-only mode, with and without the soot-induced snow albedo perturbations. The chemistry simulation shows large spatial variability in soot deposition that reflects the localized emissions and the influence of the complex terrain. The soot-induced snow albedo perturbations increase the surface net solar radiation flux during late winter to early spring, increase the surface air temperature, and reduce the snow accumulation and spring snowmelt. These effects are stronger over the central Rockies and southern Alberta, where soot deposition and snowpack overlap the most. The indirect forcing of soot accelerates snowmelt and alters stream flows, including a trend toward earlier melt dates in the western United States. The soot-induced albedo reduction initiates a positive feedback process whereby dirty snow absorbs more solar radiation, heating the surface and warming the air. This warming causes reduced snow depth and fraction, which further reduces the regional surface albedo for the snow covered regions. For a doubled snow albedo perturbation, the change to surface energy and temperature is around 50-80%, however, snowpack reduction is nonlinearly accelerated.

Relationship between worsening renal function and long-term cardiovascular mortality in heart failure patients.

PubMed

Okabe, Toshitaka; Yakushiji, Tadayuki; Kido, Takehiko; Oyama, Yuji; Igawa, Wataru; Ono, Morio; Ebara, Seitaro; Yamashita, Kennosuke; Yamamoto, Myong Hwa; Saito, Shigeo; Amemiya, Kisaki; Isomura, Naoei; Araki, Hiroshi; Ochiai, Masahiko

2017-03-01

Recently several studies showed that worsening renal function (WRF) during hospitalization might be a strong independent predictor of poor prognosis in decompensated heart failure (HF) patients. However, these studies had a relatively short follow-up duration and their data were limited to in-hospital outcomes. Our purpose was to assess the relationship between WRF and long-term cardiovascular mortality in HF patients. We enrolled decompensated HF patients who were admitted to our hospital between April 2010 and March 2015. WRF was defined as a relative increase in serum creatinine of at least 25% or an absolute increase in serum creatinine ≥0.3mg/dL from the baseline. We assessed the cardiovascular mortality and all-cause mortality in HF patients with WRF (WRF group) and without WRF (no WRF group). Among 301 patients enrolled, WRF developed in 118 patients (39.2%). During a median follow-up period of 537days [interquartile range, 304.3 to 1025.8days], cardiovascular mortality and all-cause mortality were significantly higher in the WRF group than in the no WRF group (23.2% vs. 6.1%, P<0.001; 30.3% vs. 14.7%, P<0.001, respectively). In the multivariate Cox proportional hazards model, age and serum B-type natriuretic peptide (BNP) level were associated with both cardiovascular death and all-cause death. However, WRF was not the independent predictor of cardiovascular death (P=0.19) nor all-cause death (P=0.57). WRF was associated with cardiovascular death in patients with HF. Although not an independent predictor, WRF might be one of useful markers to identify patients who should be followed carefully after discharge. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Weather Research and Forecasting Model Wind Sensitivity Study at Edwards Air Force Base, CA

NASA Technical Reports Server (NTRS)

Watson, Leela R.; Bauman, William H., III; Hoeth, Brian

2009-01-01

This abstract describes work that will be done by the Applied Meteorology Unit (AMU) in assessing the success of different model configurations in predicting "wind cycling" cases at Edwards Air Force Base, CA (EAFB), in which the wind speeds and directions oscillate among towers near the EAFB runway. The Weather Research and Forecasting (WRF) model allows users to choose among two dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM). There are also data assimilation analysis packages available for the initialization of the WRF model - the Local Analysis and Prediction System (LAPS) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS). Having a series of initialization options and WRF cores, as well as many options within each core, creates challenges for local forecasters, such as determining which configuration options are best to address specific forecast concerns. The goal of this project is to assess the different configurations available and determine which configuration will best predict surface wind speed and direction at EAFB.

Short-Term Forecasts Using NU-WRF for the Winter Olympics 2018

NASA Technical Reports Server (NTRS)

Srikishen, Jayanthi; Case, Jonathan L.; Petersen, Walter A.; Iguchi, Takamichi; Tao, Wei-Kuo; Zavodsky, Bradley T.; Molthan, Andrew

2017-01-01

The NASA Unified-Weather Research and Forecasting model (NU-WRF) will be included for testing and evaluation in the forecast demonstration project (FDP) of the International Collaborative Experiment -PyeongChang 2018 Olympic and Paralympic (ICE-POP) Winter Games. An international array of radar and supporting ground based observations together with various forecast and now-cast models will be operational during ICE-POP. In conjunction with personnel from NASA's Goddard Space Flight Center, the NASA Short-term Prediction Research and Transition (SPoRT) Center is developing benchmark simulations for a real-time NU-WRF configuration to run during the FDP. ICE-POP observational datasets will be used to validate model simulations and investigate improved model physics and performance for prediction of snow events during the research phase (RDP) of the project The NU-WRF model simulations will also support NASA Global Precipitation Measurement (GPM) Mission ground-validation physical and direct validation activities in relation to verifying, testing and improving satellite-based snowfall retrieval algorithms over complex terrain.

Mesoscale Air-Sea Interactions along the Gulf Stream: An Eddy-Resolving and Convection-Permitting Coupled Regional Climate Model Study

NASA Astrophysics Data System (ADS)

Hsieh, J. S.; Chang, P.; Saravanan, R.

2017-12-01

Frontal and mesoscale air-sea interactions along the Gulf Stream (GS) during boreal winter are investigated using an eddy-resolving and convection-permitting coupled regional climate model with atmospheric grid resolutions varying from meso-β (27-km) to -r (9-km and 3-km nest) scales in WRF and a 9-km ocean model (ROMS) that explicitly resolves the ocean mesoscale eddies across the North Atlantic basin. The mesoscale wavenumber energy spectra for the simulated surface wind stress and SST demonstrate good agreement with the observed spectra calculated from the observational QuikSCAT and AMSR-E datasets, suggesting that the model well captures the energy cascade of the mesoscale eddies in both the atmosphere and the ocean. Intercomparison among different resolution simulations indicates that after three months of integration the simulated GS path tends to overshoot beyond the separation point in the 27-km WRF coupled experiments than the observed climatological path of the GS, whereas the 3-km nested and 9-km WRF coupled simulations realistically simulate GS separation. The GS overshoot in 27-km WRF coupled simulations is accompanied with a significant SST warming bias to the north of the GS extension. Such biases are associated with the deficiency of wind stress-SST coupling strengths simulated by the coupled model with a coarser resolution in WRF. It is found that the model at 27-km grid spacing can approximately simulate 72% (62%) of the observed mean coupling strength between surface wind stress curl (divergence) and crosswind (downwind) SST gradient while by increasing the WRF resolutions to 9 km or 3 km the coupled model can much better capture the observed coupling strengths.

Forecasting near-surface weather conditions and precipitation in Alaska's Prince William Sound with the PWS-WRF modeling system

NASA Astrophysics Data System (ADS)

Olsson, Peter Q.; Volz, Karl P.; Liu, Haibo

2013-07-01

In the summer of 2009, several scientific teams engaged in a field program in Prince William Sound (PWS), Alaska to test an end-to-end atmosphere/ocean prediction system specially designed for this region. The "Sound Predictions Field Experiment" (FE) was a test of the PWS-Observing System (PWS-OS) and the culmination of a five-year program to develop an observational and prediction system for the Sound. This manuscript reports on results of an 18-day high-resolution atmospheric forecasting field project using the Weather Research and Forecasting (WRF) model.Special attention was paid to surface meteorological properties and precipitation. Upon reviewing the results of the real-time forecasts, modifications were incorporated in the PWS-WRF modeling system in an effort to improve objective forecast skill. Changes were both geometric (model grid structure) and physical (different physics parameterizations).The weather during the summer-time FE was typical of the PWS in that it was characterized by a number of minor disturbances rotating around an anchored low, but with no major storms in the Gulf of Alaska. The basic PWS-WRF modeling system as implemented operationally for the FE performed well, especially considering the extremely complex terrain comprising the greater PWS region.Modifications to the initial PWS-WRF modeling system showed improvement in predicting surface variables, especially where the ambient flow interacted strongly with the terrain. Prediction of precipitation on an accumulated basis was more accurate than prediction on a day-to-day basis. The 18-day period was too short to provide reliable assessment and intercomparison of the quantitative precipitation forecasting (QPF) skill of the PWS-WRF model variants.

Relation of Worsened Renal Function during Hospitalization for Heart Failure to Long-Term Outcomes and Rehospitalization

PubMed Central

Lanfear, David E.; Peterson, Edward L.; Campbell, Janis; Phatak, Hemant; Wu, David; Wells, Karen; Spertus, John A.; Williams, L. Keoki

2010-01-01

Worsened renal function (WRF) during heart failure (HF) hospitalization is associated with in-hospital mortality, but there are limited data regarding its relationship to long-term outcomes after discharge. The influence of WRF resolution is also unknown. This retrospective study analyzed patients who received care from a large health system and had a primary hospital discharge diagnosis of HF between 1/2000 and 6/2008. Renal function was estimated from creatinine levels during hospitalization. The first available value was considered baseline. WRF was defined a creatinine increase of ≥0.3mg/dl on any subsequent hospital day compared to baseline. Persistent WRF was defined as having WRF at discharge. Proportional hazards regression, adjusting for baseline renal function and potential confounding factors, was used to assess time to re-hospitalization or death. Among 2465 patients who survived to discharge, 887 (36%) developed WRF. Median follow up was 2.1 years. In adjusted models, WRF was associated with higher rates of post-discharge death or re-hospitalization (HR 1.12, 95%CI 1.02 – 1.22). Among those with WRF, 528 (60%) had persistent WRF while 359 (40%) recovered. Persistent WRF was significantly associated with higher post-discharge event rates (HR 1.14, 95%CI 1.02 – 1.27), whereas transient WRF showed only a non-significant trend towards risk (HR 1.09 95%CI 0.96-1.24). In conclusion, among patients surviving hospitalization for HF, WRF was associated with increased long-term mortality and re-hospitalization, particularly if renal function did not recover by the time of discharge. PMID:21146690

Effect and clinical prediction of worsening renal function in acute decompensated heart failure.

PubMed

Breidthardt, Tobias; Socrates, Thenral; Noveanu, Markus; Klima, Theresia; Heinisch, Corinna; Reichlin, Tobias; Potocki, Mihael; Nowak, Albina; Tschung, Christopher; Arenja, Nisha; Bingisser, Roland; Mueller, Christian

2011-03-01

We aimed to establish the prevalence and effect of worsening renal function (WRF) on survival among patients with acute decompensated heart failure. Furthermore, we sought to establish a risk score for the prediction of WRF and externally validate the previously established Forman risk score. A total of 657 consecutive patients with acute decompensated heart failure presenting to the emergency department and undergoing serial creatinine measurements were enrolled. The potential of the clinical parameters at admission to predict WRF was assessed as the primary end point. The secondary end point was all-cause mortality at 360 days. Of the 657 patients, 136 (21%) developed WRF, and 220 patients had died during the first year. WRF was more common in the nonsurvivors (30% vs 41%, p = 0.03). Multivariate regression analysis found WRF to independently predict mortality (hazard ratio 1.92, p <0.01). In a single parameter model, previously diagnosed chronic kidney disease was the only independent predictor of WRF and achieved an area under the receiver operating characteristic curve of 0.60. After the inclusion of the blood gas analysis parameters into the model history of chronic kidney disease (hazard ratio 2.13, p = 0.03), outpatient diuretics (hazard ratio 5.75, p <0.01), and bicarbonate (hazard ratio 0.91, p <0.01) were all predictive of WRF. A risk score was developed using these predictors. On receiver operating characteristic curve analysis, the Forman and Basel prediction rules achieved an area under the curve of 0.65 and 0.71, respectively. In conclusion, WRF was common in patients with acute decompensated heart failure and was linked to significantly worse outcomes. However, the clinical parameters failed to adequately predict its occurrence, making a tailored therapy approach impossible. Copyright © 2011 Elsevier Inc. All rights reserved.

Using JPSS VIIRS Fire Radiative Power Data to Forecast Biomass Burning Emissions and Smoke Transport by the High Resolution Rapid Refresh Model

NASA Astrophysics Data System (ADS)

Ahmadov, R.; Grell, G. A.; James, E.; Alexander, C.; Stewart, J.; Benjamin, S.; McKeen, S. A.; Csiszar, I. A.; Tsidulko, M.; Pierce, R. B.; Pereira, G.; Freitas, S. R.; Goldberg, M.

2017-12-01

We present a new real-time smoke modeling system, the High Resolution Rapid Refresh coupled with smoke (HRRR-Smoke), to simulate biomass burning (BB) emissions, plume rise and smoke transport in real time. The HRRR is the NOAA Earth System Research Laboratory's 3km grid spacing version of the Weather Research and Forecasting (WRF) model used for weather forecasting. Here we make use of WRF-Chem (the WRF model coupled with chemistry) and simulate fine particulate matter (smoke) emissions emitted by BB. The HRRR-Smoke modeling system ingests fire radiative power (FRP) data from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (S-NPP) satellite to calculate BB emissions. The FRP product is based on processing 750m resolution "M" bands. The algorithms for fire detection and FRP retrieval are consistent with those used to generate the MODIS fire detection data. For the purpose of ingesting VIIRS fire data into the HRRR-Smoke model, text files are generated to provide the location and detection confidence of fire pixels, as well as FRP. The VIIRS FRP data from the text files are processed and remapped over the HRRR-Smoke model domains. We process the FRP data to calculate BB emissions (smoldering part) and fire size for the model input. In addition, HRRR-Smoke uses the FRP data to simulate the injection height for the flaming emissions using concurrently simulated meteorological fields by the model. Currently, there are two 3km resolution domains covering the contiguous US and Alaska which are used to simulate smoke in real time. In our presentation, we focus on the CONUS domain. HRRR-Smoke is initialized 4 times per day to forecast smoke concentrations for the next 36 hours. The VIIRS FRP data, as well as near-surface and vertically integrated smoke mass concentrations are visualized for every forecast hour. These plots are provided to the public via the HRRR-Smoke web-page: https://rapidrefresh.noaa.gov/HRRRsmoke/. Model evaluations for a case study are presented, where simulated smoke concentrations are compared with hourly PM2.5 measurements from EPA's Air Quality System network. These comparisons demonstrate the model's ability in simulating high aerosol loadings during major wildfire events in the western US.

Impact of MODIS High-Resolution Sea-Surface Temperatures on WRF Forecasts at NWS Miami, FL

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; LaCasse, Katherine M.; Dembek, Scott R.; Santos, Pablo; Lapenta, William M.

2007-01-01

Over the past few years,studies at the Short-term Prediction Research and Transition (SPoRT) Center have suggested that the use of Moderate Resolution Imaging Spectroradiometer (MODIS) composite sea-surface temperature (SST) products in regional weather forecast models can have a significant positive impact on short-term numerical weather prediction in coastal regions. The recent paper by LaCasse et al. (2007, Monthly Weather Review) highlights lower atmospheric differences in regional numerical simulations over the Florida offshore waters using 2-km SST composites derived from the MODIS instrument aboard the polar-orbiting Aqua and Terra Earth Observing System satellites. To help quantify the value of this impact on NWS Weather Forecast Offices (WFOs), the SPoRT Center and the NWS WFO at Miami, FL (MIA) are collaborating on a project to investigate the impact of using the high-resolution MODIS SST fields within the Weather Research and Forecasting (WRF) prediction system. The scientific hypothesis being tested is: More accurate specification of the lower-boundary forcing within WRF will result in improved land/sea fluxes and hence, more accurate evolution of coastal mesoscale circulations and the associated sensible weather elements. The NWS MIA is currently running the WRF system in real-time to support daily forecast operations, using the National Centers for Environmental Prediction Nonhydrostatic Mesoscale Model dynamical core within the NWS Science and Training Resource Center's Environmental Modeling System (EMS) software; The EMS is a standalone modeling system capable of downloading the necessary daily datasets, and initializing, running and displaying WRF forecasts in the NWS Advanced Weather Interactive Processing System (AWIPS) with little intervention required by forecasters. Twenty-seven hour forecasts are run daily with start times of 0300,0900, 1500, and 2100 UTC on a domain with 4-km grid spacing covering the southern half of Florida and the far western portions of the Bahamas, the Florida Keys, the Straights of Florida, and adjacent waters of the Gulf of Mexico and Atlantic Ocean. Each model run is initialized using the Local Analysis and Prediction System (LAPS) analyses available in AWIPS, invoking the diabatic. "hot-start" capability. In this WRF model "hot-start", the LAPS-analyzed cloud and precipitation features are converted into model microphysics fields with enhanced vertical velocity profiles, effectively reducing the model spin-up time required to predict precipitation systems. The SSTs are initialized with the NCEP Real-Time Global (RTG) analyses at l/12 degree resolution (approx. 9 km); however, the RTG product does not exhibit fine-scale details consistent with its grid resolution. SPoRT is conducting parallel WRF EMS runs identical to the operational runs at NWS MIA in every respect except for the use of MODIS SST composites in place of the RTG product as the initial and boundary conditions over water. The MODIS SST composites for initializing the SPoRT WRF runs are generated on a 2-km grid four times daily at 0400, 0700, 1600, and 1900 UTC, based on the times of the overhead passes of the Aqua and Terra satellites. The incorporation of the MODIS SST composites into the SPoRTWRF runs is staggered such that the 0400UTC composite initializes the 0900 UTC WRF, the 0700 UTC composite initializes the 1500 UTC WRF, the 1600 UTC composite initializes the 2100 UTC WRF, and the 1900 UTC composite initializes the 0300 UTC WRF. A comparison of the SPoRT and Miami forecasts is underway in 2007, and includes quantitative verification of near-surface temperature, dewpoint, and wind forecasts at surface observation locations. In addition, particular days of interest are being analyzed to determine the impact of the MODIS SST data on the development and evolution of predicted sea/land-breeze circulations, clouds, and precipitation. This paper will present verification results comparing the NWS MIA forecasts the SPoRT experimental WRF forecasts, and highlight any substantial differences noted in the predicted mesoscale phenomena.

Comment on "Simulation of Surface Ozone Pollution in the Central Gulf Coast Region Using WRF/Chem Model: Sensitivity to PBL and Land Surface Physics"

EPA Science Inventory

A recently published meteorology and air quality modeling study has several serious deficiencies deserving comment. The study uses the weather research and forecasting/chemistry (WRF/Chem) model to compare and evaluate boundary layer and land surface modeling options. The most se...

The paradox of transient worsening renal function in patients with acute heart failure: the role of B-type natriuretic peptide and diuretic response.

PubMed

Ruocco, Gaetano; Nuti, Ranuccio; Giambelluca, Amalia; Evangelista, Isabella; De Vivo, Oreste; Daniello, Cosimo; Palazzuoli, Alberto

2017-11-01

Worsening renal function (WRF) occurs in one-third of patients hospitalized for acute decompensated heart failure. Recently, WRF was categorized in two subtypes: persistent and transient WRF. Thus, we sought to investigate the different prognostic impact of persistent vs. transient WRF; we also evaluate the relation of two WRF phenotypes with congestion, B-type natriuretic peptide (BNP) changes, and diuretic response at discharge. The prospective was a single centre study including patients screened for interventional Diur-heart failure Trial (NCT01441245). Patients were eligible if they were admitted with a primary diagnosis of acute heart failure with evidence of volume overload. Persistent WRF was defined as a sustained creatinine increase by at least 0.3 mg/dl throughout the hospitalisation; transient WRF was defined as creatinine increase by at least 0.3 mg/dl within 72 h and a return to baseline levels at discharge. Patients were followed for 6 months after discharge. Our population included 192 acute decompensated heart failure patients. In total, 61 patients developed persistent WRF and 29 developed transient WRF. Patients with persistent WRF showed a lower mean urine output with respect to the transient WRF group and patients with preserved renal function (1618 ± 374 vs. 2132 ± 392 vs. 2075 ± 442 ml; P < 0.001). Similarly, patients with transient WRF demonstrated a higher rate of BNP decrease more than 30% than seen in patients with stable creatinine levels and in the persistent WRF group (95 vs. 76 vs. 54%; P = 0.001). Univariate Cox regression analysis demonstrated that BNP decrease less than 30% [HR 2.15 (1.40-3.40); P < 0.001] and persistent WRF [HR 1.70 (1.11-2.61); P = 0.01] were related to poor outcome; conversely, transient WRF should be considered as a protective factor [HR 0.42 (0.19-0.93); P = 0.03]. In the multivariable model, only persistent WRF appeared to be related to poor prognosis [HR 1.61 (1.02-2.57); P = 0.04]. WRF occurring during hospitalization has a different significance: transient deterioration appears to be associated with a favourable clinical course; conversely, persistent WRF is related to poor outcome.

Performance of MODIS satellite and mesoscale model based land surface temperature for soil moisture deficit estimation using Neural Network

NASA Astrophysics Data System (ADS)

Srivastava, Prashant K.; Petropoulos, George P.; Gupta, Manika; Islam, Tanvir

2015-04-01

Soil Moisture Deficit (SMD) is a key variable in the water and energy exchanges that occur at the land-surface/atmosphere interface. Monitoring SMD is an alternate method of irrigation scheduling and represents the use of the suitable quantity of water at the proper time by combining measurements of soil moisture deficit. In past it is found that LST has a strong relation to SMD, which can be estimated by MODIS or numerical weather prediction model such as WRF (Weather Research and Forecasting model). By looking into the importance of SMD, this work focused on the application of Artificial Neural Network (ANN) for evaluating its capabilities towards SMD estimation using the LST data estimated from MODIS and WRF mesoscale model. The benchmark SMD estimated from Probability Distribution Model (PDM) over the Brue catchment, Southwest of England, U.K. is used for all the calibration and validation experiments. The performances between observed and simulated SMD are assessed in terms of the Nash-Sutcliffe Efficiency (NSE), the Root Mean Square Error (RMSE) and the percentage of bias (%Bias). The application of the ANN confirmed a high capability WRF and MODIS LST for prediction of SMD. Performance during the ANN calibration and validation showed a good agreement between benchmark and estimated SMD with MODIS LST information with significantly higher performance than WRF simulated LST. The work presented showed the first comprehensive application of LST from MODIS and WRF mesoscale model for hydrological SMD estimation, particularly for the maritime climate. More studies in this direction are recommended to hydro-meteorological community, so that useful information will be accumulated in the technical literature domain for different geographical locations and climatic conditions. Keyword: WRF, Land Surface Temperature, MODIS satellite, Soil Moisture Deficit, Neural Network

A comprehensive sensitivity analysis of the WRF model for air quality applications over the Iberian Peninsula

NASA Astrophysics Data System (ADS)

Borge, Rafael; Alexandrov, Vassil; José del Vas, Juan; Lumbreras, Julio; Rodríguez, Encarnacion

Meteorological inputs play a vital role on regional air quality modelling. An extensive sensitivity analysis of the Weather Research and Forecasting (WRF) model was performed, in the framework of the Integrated Assessment Modelling System for the Iberian Peninsula (SIMCA) project. Up to 23 alternative model configurations, including Planetary Boundary Layer schemes, Microphysics, Land-surface models, Radiation schemes, Sea Surface Temperature and Four-Dimensional Data Assimilation were tested in a 3 km spatial resolution domain. Model results for the most significant meteorological variables, were assessed through a series of common statistics. The physics options identified to produce better results (Yonsei University Planetary Boundary Layer, WRF Single-Moment 6-class microphysics, Noah Land-surface model, Eta Geophysical Fluid Dynamics Laboratory longwave radiation and MM5 shortwave radiation schemes) along with other relevant user settings (time-varying Sea Surface Temperature and combined grid-observational nudging) where included in a "best case" configuration. This setup was tested and found to produce more accurate estimation of temperature, wind and humidity fields at surface level than any other configuration for the two episodes simulated. Planetary Boundary Layer height predictions showed a reasonable agreement with estimations derived from routine atmospheric soundings. Although some seasonal and geographical differences were observed, the model showed an acceptable behaviour overall. Despite being useful to define the most appropriate setup of the WRF model for air quality modelling over the Iberian Peninsula, this study provides a general overview of WRF sensitivity and can constitute a reference for future mesoscale meteorological modelling exercises.

The Impact of Microphysical Schemes on Intensity and Track of Hurricane

NASA Technical Reports Server (NTRS)

Tao, W. K.; Shi, J. J.; Chen, S. S.; Lang, S.; Lin, P.; Hong, S. Y.; Peters-Lidard, C.; Hou, A.

2010-01-01

During the past decade, both research and operational numerical weather prediction models [e.g. Weather Research and Forecasting Model (WRF)] have started using more complex microphysical schemes originally developed for high-resolution cloud resolving models (CRMs) with a 1-2 km or less horizontal resolutions. The WRF is a next-generation meso-scale forecast model and assimilation system that has incorporated a modern software framework, advanced dynamics, numeric and data assimilation techniques, a multiple moveable nesting capability, and improved physical packages. The WRF model can be used for a wide range of applications, from idealized research to operational forecasting, with an emphasis on horizontal grid sizes in the range of 1-10 km. The current WRF includes several different microphysics options. At Goddard, four different cloud microphysics schemes (warm rain only, two-class of ice, two three-class of ice with either graupel or hail) are implemented into the WRF. The performances of these schemes have been compared to those from other WRF microphysics scheme options for an Atlantic hurricane case. In addition, a brief review and comparison on the previous modeling studies on the impact of microphysics schemes and microphysical processes on intensity and track of hurricane will be presented. Generally, almost all modeling studies found that the microphysics schemes did not have major impacts on track forecast, but did have more effect on the intensity. All modeling studies found that the simulated hurricane has rapid deepening and/or intensification for the warm rain-only case. It is because all hydrometeors were very large raindrops, and they fell out quickly at and near the eye-wall region. This would hydrostatically produce the lowest pressure. In addition, these modeling studies suggested that the simulated hurricane becomes unrealistically strong by removing the evaporative cooling of cloud droplets and melting of ice particles. This is due to the much weaker downdraft simulated. However, there are many differences between different modeling studies and these differences were identified and discussed.

A spatio-temporal evaluation of the WRF physical parameterisations for numerical rainfall simulation in semi-humid and semi-arid catchments of Northern China

NASA Astrophysics Data System (ADS)

Tian, Jiyang; Liu, Jia; Wang, Jianhua; Li, Chuanzhe; Yu, Fuliang; Chu, Zhigang

2017-07-01

Mesoscale Numerical Weather Prediction systems can provide rainfall products at high resolutions in space and time, playing an increasingly more important role in water management and flood forecasting. The Weather Research and Forecasting (WRF) model is one of the most popular mesoscale systems and has been extensively used in research and practice. However, for hydrologists, an unsolved question must be addressed before each model application in a different target area. That is, how are the most appropriate combinations of physical parameterisations from the vast WRF library selected to provide the best downscaled rainfall? In this study, the WRF model was applied with 12 designed parameterisation schemes with different combinations of physical parameterisations, including microphysics, radiation, planetary boundary layer (PBL), land-surface model (LSM) and cumulus parameterisations. The selected study areas are two semi-humid and semi-arid catchments located in the Daqinghe River basin, Northern China. The performance of WRF with different parameterisation schemes is tested for simulating eight typical 24-h storm events with different evenness in space and time. In addition to the cumulative rainfall amount, the spatial and temporal patterns of the simulated rainfall are evaluated based on a two-dimensional composed verification statistic. Among the 12 parameterisation schemes, Scheme 4 outperforms the other schemes with the best average performance in simulating rainfall totals and temporal patterns; in contrast, Scheme 6 is generally a good choice for simulations of spatial rainfall distributions. Regarding the individual parameterisations, Single-Moment 6 (WSM6), Yonsei University (YSU), Kain-Fritsch (KF) and Grell-Devenyi (GD) are better choices for microphysics, planetary boundary layers (PBL) and cumulus parameterisations, respectively, in the study area. These findings provide helpful information for WRF rainfall downscaling in semi-humid and semi-arid areas. The methodologies to design and test the combination schemes of parameterisations can also be regarded as a reference for generating ensembles in numerical rainfall predictions using the WRF model.

Streamflow forecasts from WRF precipitation for flood early warning in mountain tropical areas

NASA Astrophysics Data System (ADS)

Rogelis, María Carolina; Werner, Micha

2018-02-01

Numerical weather prediction (NWP) models are fundamental to extend forecast lead times beyond the concentration time of a watershed. Particularly for flash flood forecasting in tropical mountainous watersheds, forecast precipitation is required to provide timely warnings. This paper aims to assess the potential of NWP for flood early warning purposes, and the possible improvement that bias correction can provide, in a tropical mountainous area. The paper focuses on the comparison of streamflows obtained from the post-processed precipitation forecasts, particularly the comparison of ensemble forecasts and their potential in providing skilful flood forecasts. The Weather Research and Forecasting (WRF) model is used to produce precipitation forecasts that are post-processed and used to drive a hydrologic model. Discharge forecasts obtained from the hydrological model are used to assess the skill of the WRF model. The results show that post-processed WRF precipitation adds value to the flood early warning system when compared to zero-precipitation forecasts, although the precipitation forecast used in this analysis showed little added value when compared to climatology. However, the reduction of biases obtained from the post-processed ensembles show the potential of this method and model to provide usable precipitation forecasts in tropical mountainous watersheds. The need for more detailed evaluation of the WRF model in the study area is highlighted, particularly the identification of the most suitable parameterisation, due to the inability of the model to adequately represent the convective precipitation found in the study area.

Improving High-resolution Weather Forecasts using the Weather Research and Forecasting (WRF) Model with Upgraded Kain-Fritsch Cumulus Scheme

EPA Science Inventory

High-resolution weather forecasting is affected by many aspects, i.e. model initial conditions, subgrid-scale cumulus convection and cloud microphysics schemes. Recent 12km grid studies using the Weather Research and Forecasting (WRF) model have identified the importance of inco...

Improvements to the Noah Land Surface Model in WRF-CMAQ, and its Application to Future Changes in the Chesapeake Bay Region

EPA Science Inventory

Regional, state, and local environmental regulatory agencies often use Eulerian meteorological and air quality models to investigate the potential impacts of climate, emissions, and land use changes on nutrient loading and air quality. The Noah land surface model in WRF could be...

Application, evaluation and sensitivity analysis of the coupled WRF-CMAQ system from regional to urban scales

EPA Science Inventory

The Community Multiscale Air Quality (CMAQ) model is a state-of-the-science chemical transport model (CTM) capable of simulating the emission, transport and fate of numerous air pollutants. Similarly, the Weather Research and Forecasting (WRF) model is a state-of-the-science mete...

Relation of worsened renal function during hospitalization for heart failure to long-term outcomes and rehospitalization.

PubMed

Lanfear, David E; Peterson, Edward L; Campbell, Janis; Phatak, Hemant; Wu, David; Wells, Karen; Spertus, John A; Williams, L Keoki

2011-01-01

Worsened renal function (WRF) during heart failure (HF) hospitalization is associated with in-hospital mortality, but there are limited data regarding its relation to long-term outcomes after discharge. The influence of WRF resolution is also unknown. This retrospective study analyzed patients who received care from a large health system and had a primary hospital discharge diagnosis of HF from January 2000 to June 2008. Renal function was estimated from creatinine levels during hospitalization. The first available value was considered baseline. WRF was defined a creatinine increase ≥ 0.3 mg/dl on any subsequent hospital day compared to baseline. Persistent WRF was defined as having WRF at discharge. Proportional hazards regression, adjusting for baseline renal function and potential confounding factors, was used to assess time to rehospitalization or death. Of 2,465 patients who survived to discharge, 887 (36%) developed WRF. Median follow-up was 2.1 years. In adjusted models, WRF was associated with higher rates of postdischarge death or rehospitalization (hazard ratio [HR] 1.12, 95% confidence interval [CI] 1.02 to 1.22). Of those with WRF, 528 (60%) had persistent WRF, whereas 359 (40%) recovered. Persistent WRF was significantly associated with higher postdischarge event rates (HR 1.14, 95% CI 1.02 to 1.27), whereas transient WRF showed only a nonsignificant trend toward risk (HR 1.09, 95% CI 0.96 to 1.24). In conclusion, in patients surviving hospitalization for HF, WRF was associated with increased long-term mortality and rehospitalization, particularly if renal function did not recover by the time of discharge. Copyright © 2011 Elsevier Inc. All rights reserved.

PNNL: Climate Modelling

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Hazard mitigation with cloud model based rainfall and convective data

NASA Astrophysics Data System (ADS)

Gernowo, R.; Adi, K.; Yulianto, T.; Seniyatis, S.; Yatunnisa, A. A.

2018-05-01

Heavy rain in Semarang 15 January 2013 causes flood. It is related to dynamic of weather’s parameter, especially with convection process, clouds and rainfall data. In this case, weather condition analysis uses Weather Research and Forecasting (WRF) model used to analyze. Some weather’s parameters show significant result. Their fluctuations prove there is a strong convection that produces convective cloud (Cumulonimbus). Nesting and 2 domains on WRF model show good output to represent weather’s condition commonly. The results of this study different between output cloud cover rate of observation result and output of model around 6-12 hours is because spinning-up of processing. Satellite Images of MTSAT (Multifunctional Transport Satellite) are used as a verification data to prove the result of WRF. White color of satellite image is Coldest Dark Grey (CDG) that indicates there is cloud’s top. This image consolidates that the output of WRF is good enough to analyze Semarang’s condition when the case happened.

Intercomparison of microphysical datasets collected from CAIPEEX observations and WRF simulation

NASA Astrophysics Data System (ADS)

Pattnaik, S.; Goswami, B.; Kulkarni, J.

2009-12-01

In the first phase of ongoing Cloud Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX) program of Indian Institute of Tropical Meteorology (IITM), intensive cloud microphysical datasets are collected over India during the May through September, 2009. This study is designed to evaluate the forecast skills of existing cloud microphysical parameterization schemes (i.e. single moment/double moments) within the WRF-ARW model (Version 3.1.1) during different intensive observation periods (IOP) over the targeted regions spreading all across India. Basic meteorological and cloud microphysical parameters obtained from the model simulations are validated against the observed data set collected during CAIPEEX program. For this study, we have considered three IOP phases (i.e. May 23-27, June 11-15, July 3-7) carried out over northern, central and western India respectively. This study emphasizes the thrust to understand the mechanism of evolution, intensification and distribution of simulated precipitation forecast upto day four (i.e. 96 hour forecast). Efforts have also been made to carryout few important microphysics sensitivity experiments within the explicit schemes to investigate their respective impact on the formation and distribution of vital cloud parameters (e.g. cloud liquid water, frozen hydrometeors) and model rainfall forecast over the IOP regions. The characteristic features of liquid and frozen hydrometers in the pre-monsoon and monsoon regimes are examined from model forecast as well as from CAIPEEX observation data set for different IOPs. The model is integrated in a triply nested fashion with an innermost nest explicitly resolved at a horizontal resolution of 4km.In this presentation preliminary results from aforementioned research initiatives will be introduced.

Understanding High Wintertime Ozone Events over an Oil and Natural Gas Production Region from Air Quality Model Perspective

NASA Astrophysics Data System (ADS)

Ahmadov, R.; McKeen, S. A.; Trainer, M.; Banta, R. M.; Brown, S. S.; Edwards, P. M.; Frost, G. J.; Gilman, J.; Helmig, D.; Johnson, B.; Karion, A.; Koss, A.; Lerner, B. M.; Oltmans, S. J.; Roberts, J. M.; Schnell, R. C.; Veres, P. R.; Warneke, C.; Williams, E. J.; Wild, R. J.; Yuan, B.; Zamora, R. J.; Petron, G.; De Gouw, J. A.; Peischl, J.

2014-12-01

The huge increase in production of oil and natural gas has been associated with high wintertime ozone events over some parts of the western US. The Uinta Basin, UT, where oil and natural gas production is abundant experienced high ozone concentrations in winters of recent years, when cold stagnant weather conditions were prevalent. It has been very challenging for conventional air quality models to accurately simulate such wintertime ozone pollution cases. Here, a regional air quality model study was successfully conducted for the Uinta Basin by using the WRF-Chem model. For this purpose a new emission dataset for the region's oil/gas sector was built based on atmospheric in-situ measurements made during 2012 and 2013 field campaigns in the Uinta Basin. The WRF-Chem model demonstrates that the major factors driving high ozone in the Uinta Basin in winter are shallow boundary layers with light winds, high emissions of volatile organic compounds (VOC) compared to nitrogen oxides emissions from the oil and natural gas industry, enhancement of photolysis rates and reduction of O3 dry deposition due to snow cover. We present multiple sensitivity simulations to quantify the contribution of various factors driving high ozone over the Uinta Basin. The emission perturbation simulations show that the photochemical conditions in the Basin during winter of 2013 were VOC sensitive, which suggests that targeting VOC emissions would be most beneficial for regulatory purposes. Shortcomings of the emissions within the most recent US EPA (NEI-2011, version 1) inventory are also discussed.

Application of WRF - SWAT OpenMI 2.0 based models integration for real time hydrological modelling and forecasting

NASA Astrophysics Data System (ADS)

Bugaets, Andrey; Gonchukov, Leonid

2014-05-01

Intake of deterministic distributed hydrological models into operational water management requires intensive collection and inputting of spatial distributed climatic information in a timely manner that is both time consuming and laborious. The lead time of the data pre-processing stage could be essentially reduced by coupling of hydrological and numerical weather prediction models. This is especially important for the regions such as the South of the Russian Far East where its geographical position combined with a monsoon climate affected by typhoons and extreme heavy rains caused rapid rising of the mountain rivers water level and led to the flash flooding and enormous damage. The objective of this study is development of end-to-end workflow that executes, in a loosely coupled mode, an integrated modeling system comprised of Weather Research and Forecast (WRF) atmospheric model and Soil and Water Assessment Tool (SWAT 2012) hydrological model using OpenMI 2.0 and web-service technologies. Migration SWAT into OpenMI compliant involves reorganization of the model into a separate initialization, performing timestep and finalization functions that can be accessed from outside. To save SWAT normal behavior, the source code was separated from OpenMI-specific implementation into the static library. Modified code was assembled into dynamic library and wrapped into C# class implemented the OpenMI ILinkableComponent interface. Development of WRF OpenMI-compliant component based on the idea of the wrapping web-service clients into a linkable component and seamlessly access to output netCDF files without actual models connection. The weather state variables (precipitation, wind, solar radiation, air temperature and relative humidity) are processed by automatic input selection algorithm to single out the most relevant values used by SWAT model to yield climatic data at the subbasin scale. Spatial interpolation between the WRF regular grid and SWAT subbasins centroid (which are coinciding as virtual weather stations) realized as OpenMI AdaptedOutput. In order to make sure that SWAT-WRF integration technically sounds and preevaluate the impact of the climatic data resolution on the model parameters a number of test calculations were performed with different time-spatial aggregation of WRF output. Numerical experiments were carried out for the period of 2012-2013 on the Komarovka river watershed (former Primorskaya water-balance station) located in the small mountains landscapes in the western part of the Khankaiskaya plain. The watershed outlet is equipped with the automatic water level and rain gauging stations of Primorie Hydrometeorological Agency (Prigidromet http://primgidromet.ru) observation network. Spatial structure of SWAT simulation realized by ArcSWAT 2012 with 10m DEM resolution and 1:50000 soils and landuse cover. Sensitivity analysis and calibration are performed with SWAT CUP. WRF-SWAT composition is assembled in the GUI OpenMI. For the test basin in most cases the simulation results show that the predicted and measured water levels demonstrate acceptable agreement. Enforcing SWAT with WRF output avoids some semi-empirical model approximation, replaces a native weather generator for WRF forecast interval and improved upon the operational streamflow forecast. It is anticipated that leveraging direct use of the WRF variables (not only substituted standard SWAT input) will have good potential to make SWAT more physically sound.

Four dimensional data assimilation (FDDA) impacts on WRF performance in simulating inversion layer structure and distributions of CMAQ-simulated winter ozone concentrations in Uintah Basin

NASA Astrophysics Data System (ADS)

Tran, Trang; Tran, Huy; Mansfield, Marc; Lyman, Seth; Crosman, Erik

2018-03-01

Four-dimensional data assimilation (FDDA) was applied in WRF-CMAQ model sensitivity tests to study the impact of observational and analysis nudging on model performance in simulating inversion layers and O3 concentration distributions within the Uintah Basin, Utah, U.S.A. in winter 2013. Observational nudging substantially improved WRF model performance in simulating surface wind fields, correcting a 10 °C warm surface temperature bias, correcting overestimation of the planetary boundary layer height (PBLH) and correcting underestimation of inversion strengths produced by regular WRF model physics without nudging. However, the combined effects of poor performance of WRF meteorological model physical parameterization schemes in simulating low clouds, and warm and moist biases in the temperature and moisture initialization and subsequent simulation fields, likely amplified the overestimation of warm clouds during inversion days when observational nudging was applied, impacting the resulting O3 photochemical formation in the chemistry model. To reduce the impact of a moist bias in the simulations on warm cloud formation, nudging with the analysis water mixing ratio above the planetary boundary layer (PBL) was applied. However, due to poor analysis vertical temperature profiles, applying analysis nudging also increased the errors in the modeled inversion layer vertical structure compared to observational nudging. Combining both observational and analysis nudging methods resulted in unrealistically extreme stratified stability that trapped pollutants at the lowest elevations at the center of the Uintah Basin and yielded the worst WRF performance in simulating inversion layer structure among the four sensitivity tests. The results of this study illustrate the importance of carefully considering the representativeness and quality of the observational and model analysis data sets when applying nudging techniques within stable PBLs, and the need to evaluate model results on a basin-wide scale.

Evaluation of WRF-Predicted Near-Hub-Height Winds and Ramp Events over a Pacific Northwest Site with Complex Terrain

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Qing; Berg, Larry K.; Pekour, Mikhail

The WRF model version 3.3 is used to simulate near hub-height winds and power ramps utilizing three commonly used planetary boundary-layer (PBL) schemes: Mellor-Yamada-Janjic (MYJ), University of Washington (UW), and Yonsei University (YSU). The predicted winds have small mean biases compared with observations. Power ramps and step changes (changes within an hour) consistently show that the UW scheme performed better in predicting up ramps under stable conditions with higher prediction accuracy and capture rates. Both YSU and UW scheme show good performance predicting up- and down- ramps under unstable conditions with YSU being slightly better for ramp durations longer thanmore » an hour. MYJ is the most successful simulating down-ramps under stable conditions. The high wind speed and large shear associated with low-level jets are frequently associated with power ramps, and the biases in predicted low-level jet explain some of the shown differences in ramp predictions among different PBL schemes. Low-level jets were observed as low as ~200 m in altitude over the Columbia Basin Wind Energy Study (CBWES) site, located in an area of complex terrain. The shear, low-level peak wind speeds, as well as the height of maximum wind speed are not well predicted. Model simulations with 3 PBL schemes show the largest variability among them under stable conditions.« less

Retrospective evaluation of continental-scale streamflow nudging with WRF-Hydro National Water Model V1

NASA Astrophysics Data System (ADS)

McCreight, J. L.; Wu, Y.; Gochis, D.; Rafieeinasab, A.; Dugger, A. L.; Yu, W.; Cosgrove, B.; Cui, Z.; Oubeidillah, A.; Briar, D.

2016-12-01

The streamflow (discharge) data assimilation capability in version 1 of the National Water Model (NWM; a WRF-Hydro configuration) is applied and evaluated in a 5-year (2011-2015) retrospective study using NLDAS2 forcing data over CONUS. This talk will describe the NWM V1 operational nudging (continuous-time) streamflow data assimilation approach, its motivation, and its relationship to this retrospective evaluation. Results from this study will provide a an analysis-based (not forecast-based) benchmark for streamflow DA in the NWM. The goal of the assimilation is to reduce discharge bias and improve channel initial conditions for discharge forecasting (though forecasts are not considered here). The nudging method assimilates discharge observations at nearly 7,000 USGS gages (at frequency up to 1/15 minutes) to produce a (univariate) discharge reanalysis (i.e. this is the only variable affected by the assimilation). By withholding 14% nested gages throughout CONUS in a separate validation run, we evaluate the downstream impact of assimilation at upstream gages. Based on this sample, we estimate the skill of the streamflow reanalysis at ungaged locations and examine factors governing the skill of the assimilation. Comparison of assimilation and open-loop runs is presented. Performance of DA under both high and low flow regimes and selected flooding events is examined. Preliminary evaluation of nudging parameter sensitivity and its relationship to flow regime will be presented.

A Comparison of HWRF, ARW and NMM Models in Hurricane Katrina (2005) Simulation

PubMed Central

Dodla, Venkata B.; Desamsetti, Srinivas; Yerramilli, Anjaneyulu

2011-01-01

The life cycle of Hurricane Katrina (2005) was simulated using three different modeling systems of Weather Research and Forecasting (WRF) mesoscale model. These are, HWRF (Hurricane WRF) designed specifically for hurricane studies and WRF model with two different dynamic cores as the Advanced Research WRF (ARW) model and the Non-hydrostatic Mesoscale Model (NMM). The WRF model was developed and sourced from National Center for Atmospheric Research (NCAR), incorporating the advances in atmospheric simulation system suitable for a broad range of applications. The HWRF modeling system was developed at the National Centers for Environmental Prediction (NCEP) based on the NMM dynamic core and the physical parameterization schemes specially designed for tropics. A case study of Hurricane Katrina was chosen as it is one of the intense hurricanes that caused severe destruction along the Gulf Coast from central Florida to Texas. ARW, NMM and HWRF models were designed to have two-way interactive nested domains with 27 and 9 km resolutions. The three different models used in this study were integrated for three days starting from 0000 UTC of 27 August 2005 to capture the landfall of hurricane Katrina on 29 August. The initial and time varying lateral boundary conditions were taken from NCEP global FNL (final analysis) data available at 1 degree resolution for ARW and NMM models and from NCEP GFS data at 0.5 degree resolution for HWRF model. The results show that the models simulated the intensification of Hurricane Katrina and the landfall on 29 August 2005 agreeing with the observations. Results from these experiments highlight the superior performance of HWRF model over ARW and NMM models in predicting the track and intensification of Hurricane Katrina. PMID:21776239

A comparison of HWRF, ARW and NMM models in Hurricane Katrina (2005) simulation.

PubMed

Dodla, Venkata B; Desamsetti, Srinivas; Yerramilli, Anjaneyulu

2011-06-01

The life cycle of Hurricane Katrina (2005) was simulated using three different modeling systems of Weather Research and Forecasting (WRF) mesoscale model. These are, HWRF (Hurricane WRF) designed specifically for hurricane studies and WRF model with two different dynamic cores as the Advanced Research WRF (ARW) model and the Non-hydrostatic Mesoscale Model (NMM). The WRF model was developed and sourced from National Center for Atmospheric Research (NCAR), incorporating the advances in atmospheric simulation system suitable for a broad range of applications. The HWRF modeling system was developed at the National Centers for Environmental Prediction (NCEP) based on the NMM dynamic core and the physical parameterization schemes specially designed for tropics. A case study of Hurricane Katrina was chosen as it is one of the intense hurricanes that caused severe destruction along the Gulf Coast from central Florida to Texas. ARW, NMM and HWRF models were designed to have two-way interactive nested domains with 27 and 9 km resolutions. The three different models used in this study were integrated for three days starting from 0000 UTC of 27 August 2005 to capture the landfall of hurricane Katrina on 29 August. The initial and time varying lateral boundary conditions were taken from NCEP global FNL (final analysis) data available at 1 degree resolution for ARW and NMM models and from NCEP GFS data at 0.5 degree resolution for HWRF model. The results show that the models simulated the intensification of Hurricane Katrina and the landfall on 29 August 2005 agreeing with the observations. Results from these experiments highlight the superior performance of HWRF model over ARW and NMM models in predicting the track and intensification of Hurricane Katrina.

Regional Climate Modeling over the Glaciated Regions of the Canadian High Arctic

NASA Astrophysics Data System (ADS)

Gready, Benjamin P.

The Canadian Arctic Islands (CAI) contain the largest concentration of terrestrial ice outside of the continental ice sheets. Mass loss from this region has recently increased sharply due to above average summer temperatures. Thus, increasing the understanding of the mechanisms responsible for mass loss from this region is critical. Previously, Regional Climate Models (RCMs) have been utilized to estimate climatic balance over Greenland and Antarctica. This method offers the opportunity to study a full suite of climatic variables over extensive spatially distributed grids. However, there are doubts of the applicability of such models to the CAI, given the relatively complex topography of the CAI. To test RCMs in the CAI, the polar version of the regional climate model MM5 was run at high resolution over Devon Ice Cap. At low altitudes, residuals (computed through comparisons with in situ measurements) in the net radiation budget were driven primarily by residuals in net shortwave (NSW) radiation. Residuals in NSW are largely due to inaccuracies in modeled cloud cover and modeled albedo. Albedo on glaciers and ice sheets is oversimplified in Polar MM5 and its successor, the Polar version of the Weather Research and Forecast model (Polar WRF), and is an obvious place for model improvement. Subsequently, an inline parameterization of albedo for Polar WRF was developed as a function of the depth, temperature and age of snow. The parameterization was able to reproduce elevation gradients of seasonal mean albedo derived from satellite albedo measurements (MODIS MOD10A1 daily albedo), on the western slope of the Greenland Ice Sheet for three years. Feedbacks between modelled albedo and modelled surface energy budget components were identified. The shortwave radiation flux feeds back positively with changes to albedo, whereas the longwave, turbulent and ground energy fluxes all feed back negatively, with a maximum combined magnitude of two thirds of the shortwave feedback magnitude. These strong feedbacks demonstrate that an accurate albedo parameterization must be run inline within an RCM, to accurately quantify the net surface energy budget of an ice sheet. Finally, Polar WRF, with the improved albedo parameterization, was used to simulate climatic balance over the Queen Elizabeth Islands for the summers of 2001 to 2008. Climatic balance was derived from the output using energy balance and temperature index melt models. Regional mass balance was calculated by combining climatic balance with estimates of iceberg discharge. Mass balance estimates from the model agreed, within the bounds of uncertainty, with estimates from previous studies, thus supporting the assertion that mass loss from the QEI accelerated during the first decade of the 21st century. Melt rates on the seven major icecaps of the QEI became more correlated to one another during the period 2001-2008. However, precipitation became less correlated from 2003-2008. These observations are coincident with dramatic increases in melt on all of the ice caps, and it is speculated that both are caused by decreases in the scale of disturbances delivering precipitation to the region over time.

Mesoscale modelling methodology based on nudging to increase accuracy in WRA

NASA Astrophysics Data System (ADS)

Mylonas Dirdiris, Markos; Barbouchi, Sami; Hermmann, Hugo

2016-04-01

The offshore wind energy has recently become a rapidly growing renewable energy resource worldwide, with several offshore wind projects in development in different planning stages. Despite of this, a better understanding of the atmospheric interaction within the marine atmospheric boundary layer (MABL) is needed in order to contribute to a better energy capture and cost-effectiveness. Light has been thrown in observational nudging as it has recently become an innovative method to increase the accuracy of wind flow modelling. This particular study focuses on the observational nudging capability of Weather Research and Forecasting (WRF) and ways the uncertainty of wind flow modelling in the wind resource assessment (WRA) can be reduced. Finally, an alternative way to calculate the model uncertainty is pinpointed. Approach WRF mesoscale model will be nudged with observations from FINO3 at three different heights. The model simulations with and without applying observational nudging will be verified against FINO1 measurement data at 100m. In order to evaluate the observational nudging capability of WRF two ways to derive the model uncertainty will be described: one global uncertainty and an uncertainty per wind speed bin derived using the recommended practice of the IEA in order to link the model uncertainty to a wind energy production uncertainty. This study assesses the observational data assimilation capability of WRF model within the same vertical gridded atmospheric column. The principal aim is to investigate whether having observations up to one height could improve the simulation at a higher vertical level. The study will use objective analysis implementing a Cress-man scheme interpolation to interpolate the observation in time and in sp ace (keeping the horizontal component constant) to the gridded analysis. Then the WRF model core will incorporate the interpolated variables to the "first guess" to develop a nudged simulation. Consequently, WRF with and without applying observational nudging will be validated against the higher level of FINO1 met mast using verification statistical metrics such as root mean square error (RMSE), standard deviation of mean error (ME Std), mean error average (bias) and Pearson correlation coefficient (R). The respective process will be followed for different atmospheric stratification regimes in order to evaluate the sensibility of the method to the atmospheric stability. Finally, since wind speed does not have an equally distributed impact on the power yield, the uncertainty will be measured using two ways resulting in a global uncertainty and one per wind speed bin based on a wind turbine power curve in order to evaluate the WRF for the purposes of wind power generation. Conclusion This study shows the higher accuracy of the WRF model after nudging observational data. In a next step these results will be compared with traditional vertical extrapolation methods such as power and log laws. The larger picture of this work would be to nudge the observations from a short offshore metmast in order for the WRF to reconstruct accurately the entire wind profile of the atmosphere up to hub height. This is an important step in order to reduce the cost of offshore WRA. Learning objectives 1. The audience will get a clear view of the added value of observational nudging; 2. An interesting way to calculate WRF uncertainty will be described, linking wind speed uncertainty to energy uncertainty.

Technical Challenges and Solutions in Representing Lakes when using WRF in Downscaling Applications

EPA Science Inventory

The Weather Research and Forecasting (WRF) model is commonly used to make high resolution future projections of regional climate by downscaling global climate model (GCM) outputs. Because the GCM fields are typically at a much coarser spatial resolution than the target regional ...

Sensitivity of the Community Multiscale Air Quality (CMAQ) Model v4.7 Results for the Eastern United States to MM5 and WRF Meteorological Drivers

EPA Science Inventory

This paper presents a comparison of the operational performance of two Community Multiscale Air Quality (CMAQ) model v4.7 simulations that utilize input data from the 5th generation Mesoscale Model MM5 and the Weather Research and Forecasting (WRF) meteorological models.

Validation of the WRF-CMAQ Two-Way Model with Aircraft Data and High Resolution MODIS Data in the CA 2008 Wildfire Case

EPA Science Inventory

A new WRF-CMAQ two-way coupled model was developed to provide a pathway for chemical feedbacks from the air quality model to the meteorological model. The essence of this interaction is focused on the direct radiative effects of scattering and absorbing aerosols in the tropospher...

WRF added value to capture the spatio-temporal drought variability

NASA Astrophysics Data System (ADS)

García-Valdecasas Ojeda, Matilde; Quishpe-Vásquez, César; Raquel Gámiz-Fortis, Sonia; Castro-Díez, Yolanda; Jesús Esteban-Parra, María

2017-04-01

Regional Climate Models (RCM) has been widely used as a tool to perform high resolution climate fields in areas with high climate variability such as Spain. However, the outputs provided by downscaling techniques have many sources of uncertainty associated at different aspects. In this study, the ability of the Weather Research and Forecasting (WRF) model to capture drought conditions has been analyzed. The WRF simulation was carried out for a period that spanned from 1980 to 2010 over a domain centered in the Iberian Peninsula with a spatial resolution of 0.088°, and nested in the coarser EURO-CORDEX domain (0.44° spatial resolution). To investigate the spatiotemporal drought variability, the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) has been computed at two different timescales: 3- and 12-months due to its suitability to study agricultural and hydrological droughts. The drought indices computed from WRF outputs were compared with those obtained from the observational (MOTEDAS and MOPREDAS) datasets. In order to assess the added value provided by downscaled fields, these indices were also computed from the ERA-Interim Re-Analysis database, which provides the lateral and boundary conditions of the WRF simulations. Results from this study indicate that WRF provides a noticeable benefit with respect to ERA-Interim for many regions in Spain in terms of drought indices, greater for SPI than for SPEI. The improvement offered by WRF depends on the region, index and timescale analyzed, being greater at longer timescales. These findings prove the reliability of the downscaled fields to detect drought events and, therefore, it is a remarkable source of knowledge for a suitable decision making related to water-resource management. Keywords: Drought, added value, Regional Climate Models, WRF, SPEI, SPI. Acknowledgements: This work has been financed by the projects P11-RNM-7941 (Junta de Andalucía-Spain) and CGL2013-48539-R (MINECO-Spain, FEDER).

Weather Research and Forecasting Model Wind Sensitivity Study at Edwards Air Force Base, CA

NASA Technical Reports Server (NTRS)

Watson, Leela R.; Bauman, William H., III

2008-01-01

NASA prefers to land the space shuttle at Kennedy Space Center (KSC). When weather conditions violate Flight Rules at KSC, NASA will usually divert the shuttle landing to Edwards Air Force Base (EAFB) in Southern California. But forecasting surface winds at EAFB is a challenge for the Spaceflight Meteorology Group (SMG) forecasters due to the complex terrain that surrounds EAFB, One particular phenomena identified by SMG is that makes it difficult to forecast the EAFB surface winds is called "wind cycling". This occurs when wind speeds and directions oscillate among towers near the EAFB runway leading to a challenging deorbit bum forecast for shuttle landings. The large-scale numerical weather prediction models cannot properly resolve the wind field due to their coarse horizontal resolutions, so a properly tuned high-resolution mesoscale model is needed. The Weather Research and Forecasting (WRF) model meets this requirement. The AMU assessed the different WRF model options to determine which configuration best predicted surface wind speed and direction at EAFB, To do so, the AMU compared the WRF model performance using two hot start initializations with the Advanced Research WRF and Non-hydrostatic Mesoscale Model dynamical cores and compared model performance while varying the physics options.

Worsening renal function definition is insufficient for evaluating acute renal failure in acute heart failure.

PubMed

Shirakabe, Akihiro; Hata, Noritake; Kobayashi, Nobuaki; Okazaki, Hirotake; Matsushita, Masato; Shibata, Yusaku; Nishigoori, Suguru; Uchiyama, Saori; Asai, Kuniya; Shimizu, Wataru

2018-06-01

Whether or not the definition of a worsening renal function (WRF) is adequate for the evaluation of acute renal failure in patients with acute heart failure is unclear. One thousand and eighty-three patients with acute heart failure were analysed. A WRF, indicated by a change in serum creatinine ≥0.3 mg/mL during the first 5 days, occurred in 360 patients while no-WRF, indicated by a change <0.3 mg/dL, in 723 patients. Acute kidney injury (AKI) upon admission was defined based on the ratio of the serum creatinine value recorded on admission to the baseline creatinine value and placed into groups based on the degree of AKI: no-AKI (n = 751), Class R (risk; n = 193), Class I (injury; n = 41), or Class F (failure; n = 98). The patients were assigned to another set of four groups: no-WRF/no-AKI (n = 512), no-WRF/AKI (n = 211), WRF/no-AKI (n = 239), and WRF/AKI (n = 121). A multivariate logistic regression model found that no-WRF/AKI and WRF/AKI were independently associated with 365 day mortality (hazard ratio: 1.916; 95% confidence interval: 1.234-2.974 and hazard ratio: 3.622; 95% confidence interval: 2.332-5.624). Kaplan-Meier survival curves showed that the rate of any-cause death during 1 year was significantly poorer in the no-WRF/AKI and WRF/AKI groups than in the WRF/no-AKI and no-WRF/no-AKI groups and in Class I and Class F than in Class R and the no-AKI group. The presence of AKI on admission, especially Class I and Class F status, is associated with a poor prognosis despite the lack of a WRF within the first 5 days. The prognostic ability of AKI on admission may be superior to WRF within the first 5 days. © 2018 The Authors. ESC Heart Failure published by John Wiley & Sons Ltd on behalf of the European Society of Cardiology.

Worsening renal function definition is insufficient for evaluating acute renal failure in acute heart failure

PubMed Central

Hata, Noritake; Kobayashi, Nobuaki; Okazaki, Hirotake; Matsushita, Masato; Shibata, Yusaku; Nishigoori, Suguru; Uchiyama, Saori; Asai, Kuniya; Shimizu, Wataru

2018-01-01

Abstract Aims Whether or not the definition of a worsening renal function (WRF) is adequate for the evaluation of acute renal failure in patients with acute heart failure is unclear. Methods and results One thousand and eighty‐three patients with acute heart failure were analysed. A WRF, indicated by a change in serum creatinine ≥0.3 mg/mL during the first 5 days, occurred in 360 patients while no‐WRF, indicated by a change <0.3 mg/dL, in 723 patients. Acute kidney injury (AKI) upon admission was defined based on the ratio of the serum creatinine value recorded on admission to the baseline creatinine value and placed into groups based on the degree of AKI: no‐AKI (n = 751), Class R (risk; n = 193), Class I (injury; n = 41), or Class F (failure; n = 98). The patients were assigned to another set of four groups: no‐WRF/no‐AKI (n = 512), no‐WRF/AKI (n = 211), WRF/no‐AKI (n = 239), and WRF/AKI (n = 121). A multivariate logistic regression model found that no‐WRF/AKI and WRF/AKI were independently associated with 365 day mortality (hazard ratio: 1.916; 95% confidence interval: 1.234–2.974 and hazard ratio: 3.622; 95% confidence interval: 2.332–5.624). Kaplan–Meier survival curves showed that the rate of any‐cause death during 1 year was significantly poorer in the no‐WRF/AKI and WRF/AKI groups than in the WRF/no‐AKI and no‐WRF/no‐AKI groups and in Class I and Class F than in Class R and the no‐AKI group. Conclusions The presence of AKI on admission, especially Class I and Class F status, is associated with a poor prognosis despite the lack of a WRF within the first 5 days. The prognostic ability of AKI on admission may be superior to WRF within the first 5 days. PMID:29388735

Future Drought Projections over the Iberian Peninsula using Drought Indices

NASA Astrophysics Data System (ADS)

Garcia-Valdecasas Ojeda, M.; Yeste Donaire, P.; Góngora García, T. M.; Gámiz-Fortis, S. R.; Castro-Diez, Y.; Esteban-Parra, M. J.

2017-12-01

Currently, drought events are the cause of numerous annual economic losses. In a context of climate change, it is expected an increase in the severity and the frequency of drought occurrences, especially in areas such as the Mediterranean region. This study makes use of two drought indices in order to analyze the potential changes on future drought events and their effects at different time scales over a vulnerable region, the Iberian Peninsula. The indices selected were the Standardized Precipitation Evapotranspiration Index (SPEI), which takes into account the global warming through the temperature, and the Standardized Precipitation Index (SPI), based solely on precipitation data, at a spatial resolution of 0.088º ( 10 km). For their computation, current (1980-2014) and future (2021-2050 and 2071-2100) high resolution simulations were carried out using the Weather Research and Forecasting (WRF) model over a domain centered in the Iberian Peninsula, and nested in the 0.44 EUROCORDEX region. WRF simulations were driven by two different global bias-corrected climate models: the version 1 of NCAR's Community Earth System Model (CESM1) and the Max Planck Institute's Earth System Model (MPI-ESM-LR), and under two different Representative Concentration Pathway (RCP) scenarios: RCP 4.5 and RCP 8.5. Future projections were analyzed regarding to changes in mean, median and variance of drought indices with respect to the historical distribution, as well as changes in the frequency and duration of moderate and severe drought events. In general, results suggest an increase in frequency and severity of drought, especially for 2071-2100 period in the RCP 8.5 scenario. Results also shown an increase of drought phenomena more evident using the SPEI. Conclusions from this study could provide a valuable contribution to the understanding of how the increase of the temperature would affect the drought variability in the Mediterranean regions which is necessary for a suitable decision making.Keywords: Drought, SPEI, SPI, Climatic change, Regional projections, WRF.ACKNOWLEDGEMENTS: This work has been financed by the projects P11-RNM-7941 (Junta de Andalucía) and CGL2013-48539-R (MINECO-Spain, FEDER). This analysis was carried out in the ALHAMBRA computer infrastructure at the University of Granada.

Precipitation characteristics of CAM5 physics at mesoscale resolution during MC3E and the impact of convective timescale choice

DOE PAGES

Gustafson, William I.; Ma, Po-Lun; Singh, Balwinder

2014-12-17

The physics suite of the Community Atmosphere Model version 5 (CAM5) has recently been implemented in the Weather Research and Forecasting (WRF) model to explore the behavior of the parameterization suite at high resolution and in the more controlled setting of a limited area model. The initial paper documenting this capability characterized the behavior for northern high latitude conditions. This present paper characterizes the precipitation characteristics for continental, mid-latitude, springtime conditions during the Midlatitude Continental Convective Clouds Experiment (MC3E) over the central United States. This period exhibited a range of convective conditions from those driven strongly by large-scale synoptic regimesmore » to more locally driven convection. The study focuses on the precipitation behavior at 32 km grid spacing to better anticipate how the physics will behave in the global model when used at similar grid spacing in the coming years. Importantly, one change to the Zhang-McFarlane deep convective parameterization when implemented in WRF was to make the convective timescale parameter an explicit function of grid spacing. This study examines the sensitivity of the precipitation to the default value of the convective timescale in WRF, which is 600 seconds for 32 km grid spacing, to the value of 3600 seconds used for 2 degree grid spacing in CAM5. For comparison, an infinite convective timescale is also used. The results show that the 600 second timescale gives the most accurate precipitation over the central United States in terms of rain amount. However, this setting has the worst precipitation diurnal cycle, with the convection too tightly linked to the daytime surface heating. Longer timescales greatly improve the diurnal cycle but result in less precipitation and produce a low bias. An analysis of rain rates shows the accurate precipitation amount with the shorter timescale is assembled from an over abundance of drizzle combined with too little heavy rain events. With longer timescales one can improve the distribution, particularly for the extreme rain rates. Ultimately, without changing other aspects of the physics, one must choose between accurate diurnal timing and rain amount when choosing an appropriate convective timescale.« less

High Resolution Modeling in Mountainous Terrain for Water Resource Management: AN Extreme Precipitation Event Case Study

NASA Astrophysics Data System (ADS)

Masarik, M. T.; Watson, K. A.; Flores, A. N.; Anderson, K.; Tangen, S.

2016-12-01

The water resources infrastructure of the Western US is designed to deliver reliable water supply to users and provide recreational opportunities for the public, as well as afford flood control for communities by buffering variability in precipitation and snow storage. Thus water resource management is a balancing act of meeting multiple objectives while trying to anticipate and mitigate natural variability of water supply. Currently, the forecast guidance available to personnel managing resources in mountainous terrain is lacking in two ways: the spatial resolution is too coarse, and there is a gap in the intermediate time range (10-30 days). To address this need we examine the effectiveness of using the Weather Research and Forecasting (WRF) model, a state of the art, regional, numerical weather prediction model, as a means to generate high-resolution weather guidance in the intermediate time range. This presentation will focus on a reanalysis and hindcasting case study of the extreme precipitation and flooding event in the Payette River Basin of Idaho during the period of June 2nd-4th, 2010. For the reanalysis exercise we use NCEP's Climate Forecast System Reanalysis (CFSR) and the North American Regional Reanalysis (NARR) data sets as input boundary conditions to WRF. The model configuration includes a horizontal spatial resolution of 3km in the outer nest, and 1 km in the inner nest, with output temporal resolution of 3 hrs and 1 hr, respectively. The hindcast simulations, which are currently underway, will make use of the NCEP Climate Forecast System Reforecast (CFSRR) data. The current state of these runs will be discussed. Preparations for the second of two components in this project, weekly WRF forecasts during the intense portion of the water year, will be briefly described. These forecasts will use the NCEP Climate Forecast System version 2 (CFSv2) operational forecast data as boundary conditions to provide forecast guidance geared towards water resource managers out to a lead time of 30 days. We are particularly interested in the degree to which there is forecast skill in basinwide precipitation occurrence, departure from climatology, timing, and amount in the intermediate time range.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gustafson, William I.; Ma, Po-Lun; Singh, Balwinder

The physics suite of the Community Atmosphere Model version 5 (CAM5) has recently been implemented in the Weather Research and Forecasting (WRF) model to explore the behavior of the parameterization suite at high resolution and in the more controlled setting of a limited area model. The initial paper documenting this capability characterized the behavior for northern high latitude conditions. This present paper characterizes the precipitation characteristics for continental, mid-latitude, springtime conditions during the Midlatitude Continental Convective Clouds Experiment (MC3E) over the central United States. This period exhibited a range of convective conditions from those driven strongly by large-scale synoptic regimesmore » to more locally driven convection. The study focuses on the precipitation behavior at 32 km grid spacing to better anticipate how the physics will behave in the global model when used at similar grid spacing in the coming years. Importantly, one change to the Zhang-McFarlane deep convective parameterization when implemented in WRF was to make the convective timescale parameter an explicit function of grid spacing. This study examines the sensitivity of the precipitation to the default value of the convective timescale in WRF, which is 600 seconds for 32 km grid spacing, to the value of 3600 seconds used for 2 degree grid spacing in CAM5. For comparison, an infinite convective timescale is also used. The results show that the 600 second timescale gives the most accurate precipitation over the central United States in terms of rain amount. However, this setting has the worst precipitation diurnal cycle, with the convection too tightly linked to the daytime surface heating. Longer timescales greatly improve the diurnal cycle but result in less precipitation and produce a low bias. An analysis of rain rates shows the accurate precipitation amount with the shorter timescale is assembled from an over abundance of drizzle combined with too little heavy rain events. With longer timescales one can improve the distribution, particularly for the extreme rain rates. Ultimately, without changing other aspects of the physics, one must choose between accurate diurnal timing and rain amount when choosing an appropriate convective timescale.« less

Evaluation of NU-WRF Rainfall Forecasts for IFloodS

NASA Technical Reports Server (NTRS)

Wu, Di; Peters-Lidard, Christa; Tao, Wei-Kuo; Petersen, Walter

2016-01-01

The Iowa Flood Studies (IFloodS) campaign was conducted in eastern Iowa as a pre- GPM-launch campaign from 1 May to 15 June 2013. During the campaign period, real time forecasts are conducted utilizing NASA-Unified Weather Research and Forecasting (NU-WRF) model to support the everyday weather briefing. In this study, two sets of the NU-WRF rainfall forecasts are evaluated with Stage IV and Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE), with the objective to understand the impact of Land Surface initialization on the predicted precipitation. NU-WRF is also compared with North American Mesoscale Forecast System (NAM) 12 kilometer forecast. In general, NU-WRF did a good job at capturing individual precipitation events. NU-WRF is also able to replicate a better rainfall spatial distribution compare with NAM. Further sensitivity tests show that the high-resolution makes a positive impact on rainfall forecast. The two sets of NU-WRF simulations produce very close rainfall characteristics. The Land surface initialization do not show significant impact on short term rainfall forecast, and it is largely due to the soil conditions during the field campaign period.

Evaluating the extreme precipitation events using a mesoscale atmopshere model

NASA Astrophysics Data System (ADS)

Yucel, I.; Onen, A.

2012-04-01

Evidence is showing that global warming or climate change has a direct influence on changes in precipitation and the hydrological cycle. Extreme weather events such as heavy rainfall and flooding are projected to become much more frequent as climate warms. Mesoscale atmospheric models coupled with land surface models provide efficient forecasts for meteorological events in high lead time and therefore they should be used for flood forecasting and warning issues as they provide more continuous monitoring of precipitation over large areas. This study examines the performance of the Weather Research and Forecasting (WRF) model in producing the temporal and spatial characteristics of the number of extreme precipitation events observed in West Black Sea Region of Turkey. Extreme precipitation events usually resulted in flood conditions as an associated hydrologic response of the basin. The performance of the WRF system is further investigated by using the three dimensional variational (3D-VAR) data assimilation scheme within WRF. WRF performance with and without data assimilation at high spatial resolution (4 km) is evaluated by making comparison with gauge precipitation and satellite-estimated rainfall data from Multi Precipitation Estimates (MPE). WRF-derived precipitation showed capabilities in capturing the timing of the precipitation extremes and in some extent spatial distribution and magnitude of the heavy rainfall events. These precipitation characteristics are enhanced with the use of 3D-VAR scheme in WRF system. Data assimilation improved area-averaged precipitation forecasts by 9 percent and at some points there exists quantitative match in precipitation events, which are critical for hydrologic forecast application.

Impact of implementation of spaceborne lidar-retrieved canopy height in the WRF model

NASA Astrophysics Data System (ADS)

Lee, Junhong; Hong, Jinkyu

2017-04-01

Canopy height is closely related to biomass and aerodynamic properties, which regulate turbulent transfer of energy and mass at the soil-vegetation-atmosphere continuum. However, this key information has been prescribed as a constant value in a fixed plant functional type in atmospheric models. This presentation reports impacts of using realistic forest canopy height, retrieved from spaceborne LiDAR, on regional climate simulation in the Weather Research and Forecasting (WRF) model's land surface model. Numerical simulations were conducted over the Amazon Basin and East Asia during summer season. Over these regions, the LiDAR-retrieved canopy heights were higher than the default values used in the WRF,which are dependent only on plant functional type. By modifying roughness length and zero-plane displacement height, the change of canopy height resulted in changes in surface energy balance by regulating aerodynamic conductances and vertical temperature gradient, thus modifying the lifting condensation level and equivalent potential temperature in the atmospheric boundary layer. Our analysis also showed that the WRF model better reproduced the observed precipitation when LiDAR-retrieved canopy height was used over the Amazon Basin.

Implementation of spaceborne lidar-retrieved canopy height in the WRF model

NASA Astrophysics Data System (ADS)

Lee, Junhong; Hong, Jinkyu

2016-06-01

Canopy height is closely related to biomass and aerodynamic properties, which regulate turbulent transfer of energy and mass at the soil-vegetation-atmosphere continuum. However, this key information has been prescribed as a constant value in a fixed plant functional type in atmospheric models. This paper is the first to report impacts of using realistic forest canopy height, retrieved from spaceborne lidar, on regional climate simulation by using the canopy height data in the Weather Research and Forecasting (WRF) model's land surface model. Numerical simulations were conducted over the Amazon Basin during summer season. Over this region, the lidar-retrieved canopy heights were higher than the default values used in the WRF, which are dependent only on plant functional type. By modifying roughness length and zero-plane displacement height, the change of canopy height resulted in changes in surface energy balance by regulating aerodynamic conductances and vertical temperature gradient, thus modifying the lifting condensation level and equivalent potential temperature in the atmospheric boundary layer. Our analysis also showed that the WRF model better reproduced the observed precipitation when lidar-retrieved canopy height was used over the Amazon Basin.

Evaluation of WRF Model Against Satellite and Field Measurements During ARM March 2000 IOP

NASA Astrophysics Data System (ADS)

Wu, J.; Zhang, M.

2003-12-01

Meso-scale WRF model is employed to simulate the organization of clouds related with the cyclogenesis occurred during March 1-4, 2000 over ARM SGP CART site. Qualitative comparisons of simulated clouds with GOES8 satellite images show that the WRF model can capture the main features of clouds related with the cyclogenesis. The simulated precipitation patterns also match the Radar reflectivity images well. Further evaluation of the simulated features on GCM grid-scale is conducted against ARM field measurements. The evaluation shows that the evolutions of the simulated state fields such as temperature and moisture, the simulated wind fields and the derived large-scale temperature and moisture tendencies closely follow the observed patterns. These results encourages us to use meso-scale WRF model as a tool to verify the performance of GCMs in simulating cloud feedback processes related with the frontal clouds such that we can test and validate the current cloud parameterizations in climate models, and make possible improvements to different components of current cloud parameterizations in GCMs.

Assessment of the Aerosol Optics Component of the Coupled WRF-CMAQ Model usingCARES Field Campaign data and a Single Column Model

EPA Science Inventory

The Carbonaceous Aerosols and Radiative Effects Study (CARES), a field campaign held in central California in June 2010, provides a unique opportunity to assess the aerosol optics modeling component of the two-way coupled Weather Research and Forecasting (WRF) – Community Multisc...

A new chemistry option in WRF-Chem v. 3.4 for the simulation of direct and indirect aerosol effects using VBS: evaluation against IMPACT-EUCAARI data

NASA Astrophysics Data System (ADS)

Tuccella, P.; Curci, G.; Grell, G. A.; Visconti, G.; Crumeyrolle, S.; Schwarzenboeck, A.; Mensah, A. A.

2015-09-01

A parameterization for secondary organic aerosol (SOA) production based on the volatility basis set (VBS) approach has been coupled with microphysics and radiative schemes in the Weather Research and Forecasting model with Chemistry (WRF-Chem) model. The new chemistry option called "RACM-MADE-VBS-AQCHEM" was evaluated on a cloud resolving scale against ground-based and aircraft measurements collected during the IMPACT-EUCAARI (Intensive Cloud Aerosol Measurement Campaign - European Integrated project on Aerosol Cloud Climate and Air quality interaction) campaign, and complemented with satellite data from MODIS. The day-to-day variability and the diurnal cycle of ozone (O3) and nitrogen oxides (NOx) at the surface are captured by the model. Surface aerosol mass concentrations of sulfate (SO4), nitrate (NO3), ammonium (NH4), and organic matter (OM) are simulated with correlations larger than 0.55. WRF-Chem captures the vertical profile of the aerosol mass concentration in both the planetary boundary layer (PBL) and free troposphere (FT) as a function of the synoptic condition, but the model does not capture the full range of the measured concentrations. Predicted OM concentration is at the lower end of the observed mass concentrations. The bias may be attributable to the missing aqueous chemistry processes of organic compounds and to uncertainties in meteorological fields. A key role could be played by assumptions on the VBS approach such as the SOA formation pathways, oxidation rate, and dry deposition velocity of organic condensable vapours. Another source of error in simulating SOA is the uncertainties in the anthropogenic emissions of primary organic carbon. Aerosol particle number concentration (condensation nuclei, CN) is overestimated by a factor of 1.4 and 1.7 within the PBL and FT, respectively. Model bias is most likely attributable to the uncertainties of primary particle emissions (mostly in the PBL) and to the nucleation rate. Simulated cloud condensation nuclei (CCN) are also overestimated, but the bias is more contained with respect to that of CN. The CCN efficiency, which is a characterization of the ability of aerosol particles to nucleate cloud droplets, is underestimated by a factor of 1.5 and 3.8 in the PBL and FT, respectively. The comparison with MODIS data shows that the model overestimates the aerosol optical thickness (AOT). The domain averages (for 1 day) are 0.38 ± 0.12 and 0.42 ± 0.10 for MODIS and WRF-Chem data, respectively. The droplet effective radius (Re) in liquid-phase clouds is underestimated by a factor of 1.5; the cloud liquid water path (LWP) is overestimated by a factor of 1.1-1.6. The consequence is the overestimation of average liquid cloud optical thickness (COT) from a few percent up to 42 %. The predicted cloud water path (CWP) in all phases displays a bias in the range +41-80 %, whereas the bias of COT is about 15 %. In sensitivity tests where we excluded SOA, the skills of the model in reproducing the observed patterns and average values of the microphysical and optical properties of liquid and all phase clouds decreases. Moreover, the run without SOA (NOSOA) shows convective clouds with an enhanced content of liquid and frozen hydrometers, and stronger updrafts and downdrafts. Considering that the previous version of WRF-Chem coupled with a modal aerosol module predicted very low SOA content (secondary organic aerosol model (SORGAM) mechanism) the new proposed option may lead to a better characterization of aerosol-cloud feedbacks.

An Observation-base investigation of nudging in WRF for downscaling surface climate information to 12-km Grid Spacing

EPA Science Inventory

Previous research has demonstrated the ability to use the Weather Research and Forecast (WRF) model and contemporary dynamical downscaling methods to refine global climate modeling results to a horizontal resolution of 36 km. Environmental managers and urban planners have expre...

Tropical Cyclone Prediction Using COAMPS-TC

DTIC Science & Technology

2014-09-01

landfalling hurricanes with the advanced hurricane WRF model. Monthly Weather Review 136:1,990–2,005, http://dx.doi.org/10.1175/2007MWR2085.1. DeMaria, M...Weisman. 2004. The next generation of NWP: Explicit forecasts of convection using the Weather Research and Forecast ( WRF ) Model. Atmospheric Science

Sensitivity of WRF Regional Climate Simulations to Choice of Land Use Dataset

EPA Science Inventory

The goal of this study is to assess the sensitivity of regional climate simulations run with the Weather Research and Forecasting (WRF) model to the choice of datasets representing land use and land cover (LULC). Within a regional climate modeling application, an accurate repres...

“Assessment of the two-way Coupled WRF-CMAQ Model with Observations from the CARES”

EPA Science Inventory

The main goal of this assessment is to evaluate the improved aerosol component of two-way coupled WRF-CMAQ model particularly in representing aerosol physical and optical properties by utilizing observations from the Carbonaceous Aerosol and Radiative Effects Study (CARES) in May...

Comparison of Spatial and Temporal Rainfall Characteristics in WRF-Simulated Precipitation to Gauge and Radar Observations

EPA Science Inventory

Weather Research and Forecasting (WRF) meteorological data are used for USEPA multimedia air and water quality modeling applications, within the CMAQ modeling system to estimate wet deposition and to evaluate future climate and land-use scenarios. While it is not expected that hi...

Application and evaluation of high-resolution WRF-CMAQ with simple urban parameterization.

EPA Science Inventory

The 2-way coupled WRF-CMAQ meteorology and air quality modeling system is evaluated for high-resolution applications by comparing to a regional air quality field study (Discover-AQ). The model was modified to better account for the effects of urban environments. High-resolution...

Application and evaluation of high-resolution WRF-CMAQ with simple urban parameterization

EPA Science Inventory

The 2-way coupled WRF-CMAQ meteorology and air quality modeling system is evaluated for high-resolution applications by comparing to a regional air quality field study (Discover-AQ). The model was modified to better account for the effects of urban environments. High-resolution...

Comparison of Two Grid Refinement Approaches for High Resolution Regional Climate Modeling: MPAS vs WRF

NASA Astrophysics Data System (ADS)

Leung, L.; Hagos, S. M.; Rauscher, S.; Ringler, T.

2012-12-01

This study compares two grid refinement approaches using global variable resolution model and nesting for high-resolution regional climate modeling. The global variable resolution model, Model for Prediction Across Scales (MPAS), and the limited area model, Weather Research and Forecasting (WRF) model, are compared in an idealized aqua-planet context with a focus on the spatial and temporal characteristics of tropical precipitation simulated by the models using the same physics package from the Community Atmosphere Model (CAM4). For MPAS, simulations have been performed with a quasi-uniform resolution global domain at coarse (1 degree) and high (0.25 degree) resolution, and a variable resolution domain with a high-resolution region at 0.25 degree configured inside a coarse resolution global domain at 1 degree resolution. Similarly, WRF has been configured to run on a coarse (1 degree) and high (0.25 degree) resolution tropical channel domain as well as a nested domain with a high-resolution region at 0.25 degree nested two-way inside the coarse resolution (1 degree) tropical channel. The variable resolution or nested simulations are compared against the high-resolution simulations that serve as virtual reality. Both MPAS and WRF simulate 20-day Kelvin waves propagating through the high-resolution domains fairly unaffected by the change in resolution. In addition, both models respond to increased resolution with enhanced precipitation. Grid refinement induces zonal asymmetry in precipitation (heating), accompanied by zonal anomalous Walker like circulations and standing Rossby wave signals. However, there are important differences between the anomalous patterns in MPAS and WRF due to differences in the grid refinement approaches and sensitivity of model physics to grid resolution. This study highlights the need for "scale aware" parameterizations in variable resolution and nested regional models.

Validation of WRF forecasts for the Chajnantor region

NASA Astrophysics Data System (ADS)

Pozo, Diana; Marín, J. C.; Illanes, L.; Curé, M.; Rabanus, D.

2016-06-01

This study assesses the performance of the Weather Research and Forecasting (WRF) model to represent the near-surface weather conditions and the precipitable water vapour (PWV) in the Chajnantor plateau, in the north of Chile, from 2007 April to December. The WRF model shows a very good performance forecasting the near-surface temperature and zonal wind component, although it overestimates the 2 m water vapour mixing ratio and underestimates the 10 m meridional wind component. The model represents very well the seasonal, intraseasonal and the diurnal variation of PWV. However, the PWV errors increase after the 12 h of simulation. Errors in the simulations are larger than 1.5 mm only during 10 per cent of the study period, they do not exceed 0.5 mm during 65 per cent of the time and they are below 0.25 mm more than 45 per cent of the time, which emphasizes the good performance of the model to forecast the PWV over the region. The misrepresentation of the near-surface humidity in the region by the WRF model may have a negative impact on the PWV forecasts. Thus, having accurate forecasts of humidity near the surface may result in more accurate PWV forecasts. Overall, results from this, as well as recent studies, supports the use of the WRF model to provide accurate weather forecasts for the region, particularly for the PWV, which can be of great benefit for astronomers in the planning of their scientific operations and observing time.

A Method for Evaluation of Model-Generated Vertical Profiles of Meteorological Variables

DTIC Science & Technology

2016-03-01

3 2.1 RAOB Soundings and WRF Output for Profile Generation 3 2.2 Height-Based Profiles 5 2.3 Pressure-Based Profiles 5 3. Comparisons 8 4...downward arrow. The blue lines represent sublayers with sublayer means indicated by red triangles. Circles indicate the observations or WRF output...9 Table 3 Sample of differences in listed variables derived from WRF and RAOB data

Quality and sensitivity of high-resolution numerical simulation of urban heat islands

NASA Astrophysics Data System (ADS)

Li, Dan; Bou-Zeid, Elie

2014-05-01

High-resolution numerical simulations of the urban heat island (UHI) effect with the widely-used Weather Research and Forecasting (WRF) model are assessed. Both the sensitivity of the results to the simulation setup, and the quality of the simulated fields as representations of the real world, are investigated. Results indicate that the WRF-simulated surface temperatures are more sensitive to the planetary boundary layer (PBL) scheme choice during nighttime, and more sensitive to the surface thermal roughness length parameterization during daytime. The urban surface temperatures simulated by WRF are also highly sensitive to the urban canopy model (UCM) used. The implementation in this study of an improved UCM (the Princeton UCM or PUCM) that allows the simulation of heterogeneous urban facets and of key hydrological processes, together with the so-called CZ09 parameterization for the thermal roughness length, significantly reduce the bias (<1.5 °C) in the surface temperature fields as compared to satellite observations during daytime. The boundary layer potential temperature profiles are captured by WRF reasonable well at both urban and rural sites; the biases in these profiles relative to aircraft-mounted senor measurements are on the order of 1.5 °C. Changing UCMs and PBL schemes does not alter the performance of WRF in reproducing bulk boundary layer temperature profiles significantly. The results illustrate the wide range of urban environmental conditions that various configurations of WRF can produce, and the significant biases that should be assessed before inferences are made based on WRF outputs. The optimal set-up of WRF-PUCM developed in this paper also paves the way for a confident exploration of the city-scale impacts of UHI mitigation strategies in the companion paper (Li et al 2014).

Impacts of subgrid-scale orography parameterization on simulated atmospheric fields over Korea using a high-resolution atmospheric forecast model

NASA Astrophysics Data System (ADS)

Lim, Kyo-Sun Sunny; Lim, Jong-Myoung; Shin, Hyeyum Hailey; Hong, Jinkyu; Ji, Young-Yong; Lee, Wanno

2018-06-01

A substantial over-prediction bias at low-to-moderate wind speeds in the Weather Research and Forecasting (WRF) model has been reported in the previous studies. Low-level wind fields play an important role in dispersion of air pollutants, including radionuclides, in a high-resolution WRF framework. By implementing two subgrid-scale orography parameterizations (Jimenez and Dudhia in J Appl Meteorol Climatol 51:300-316, 2012; Mass and Ovens in WRF model physics: problems, solutions and a new paradigm for progress. Preprints, 2010 WRF Users' Workshop, NCAR, Boulder, Colo. http://www.mmm.ucar.edu/wrf/users/workshops/WS2010/presentations/session%204/4-1_WRFworkshop2010Final.pdf, 2010), we tried to compare the performance of parameterizations and to enhance the forecast skill of low-level wind fields over the central western part of South Korea. Even though both subgrid-scale orography parameterizations significantly alleviated the positive bias at 10-m wind speed, the parameterization by Jimenez and Dudhia revealed a better forecast skill in wind speed under our modeling configuration. Implementation of the subgrid-scale orography parameterizations in the model did not affect the forecast skills in other meteorological fields including 10-m wind direction. Our study also brought up the problem of discrepancy in the definition of "10-m" wind between model physics parameterizations and observations, which can cause overestimated winds in model simulations. The overestimation was larger in stable conditions than in unstable conditions, indicating that the weak diurnal cycle in the model could be attributed to the representation error.

A Step towards a Sharable Community Knowledge Base for WRF Settings -Developing a WRF Setting Methodology based on a case study in a Torrential Rainfall Event

NASA Astrophysics Data System (ADS)

CHU, Q.; Xu, Z.; Zhuo, L.; Han, D.

2016-12-01

Increased requirements for interactions between different disciplines and readily access to the numerical weather forecasting system featured with portability and extensibility have made useful contribution to the increases of downstream model users in WRF over recent years. For these users, a knowledge base classified by the representative events would be much helpful. This is because the determination of model settings is regarded as the most important steps in WRF. However, such a process is generally time-consuming, even if with a high computational platform. As such, we propose a sharable proper lookup table on WRF domain settings and corresponding procedures based on a representative torrential rainfall event in Beijing, China. It has been found that WRF's simulations' drift away from the input lateral boundary conditions can be significantly reduced with the adjustment of the domain settings. Among all the impact factors, the placement of nested domain can not only affect the moving speed and angle of the storm-center, but also the location and amount of heavy-rain-belt which can only be detected with adjusted spatial resolutions. Spin-up time is also considered in the model settings, which is demonstrated to have the most obvious influence on the accuracy of the simulations. This conclusion is made based on the large diversity of spatial distributions of precipitation, in terms of the amount of heavy rain varied from -30% to 58% among each experiment. After following all the procedures, the variations of domain settings have minimal effect on the modeling and show the best correlation (larger than 0.65) with fusion observations. So the model settings, including domain size covering the greater Beijing area, 1:5:5 downscaling ratio, 57 vertical levels with top of 50hpa and 60h spin-up time, are found suitable for predicting the similar convective torrential rainfall event in Beijing area. We hope that the procedure for building the community WRF knowledge base in this paper would be helpful to peer-researchers and operational communities by saving them from repeating each other's work. More importantly, the results by studying different events and locations could enrich this community knowledge base to benefit WRF users around the world in the future.

A climatology and preliminary investigation of predictability of pristine nocturnal convective initiation in the central United States

NASA Astrophysics Data System (ADS)

Stelten, Sean; Gallus, William

2017-04-01

The prediction of convective initiation remains a challenge to forecasters in the central United States, especially for elevated events at night. This study examines a subset of 287 nocturnal elevated convective initiation events that occurred without direct influence from surface boundaries or pre-existing convection over a four-month period during the summer of 2015 (May, June, July, and August). Events were first classified into one of four types based on apparent formation mechanisms and location relative to any low-level jet. A climatology of each of the four types was performed focusing on general spatial tendencies over the central United States and initiation timing trends. Additionally, analysis of initiation elevation was performed. Simulations from five convection-allowing models available during the Plains Elevated Convection At Night (PECAN) field campaign, along with four versions of a 4km horizontal grid spacing Weather Research and Forecasting (WRF) model using different planetary boundary layer (PBL) parameterizations, were used to examine predictability of these types of convective initiation. The climatology revealed a dual-peak pattern for initiation timing with one peak near 0400 UTC and another 0700 UTC, and it was found that the dual peak structure was present for all four types of events, suggesting that the evolution of the low-level jet was not directly responsible for the twin peaks. Subtle differences in location and elevation of the initiation for the different types were identified. The convection-allowing models run during the PECAN project were found to be more deficient with location than timing. Threat scores typically averaged around 0.3 for the models, with false alarm ratios and hit rates both averaging around 0.5 to 0.6 for the various models. Initiation occurring within the low-level jet but far from a surface front was the one type that was occasionally missed by all five models examined. Once case for each of the four types was then simulated with four different configurations of a 4 km horizontal grid spacing WRF model. These WRF runs showed similar location errors and problems with initiating convection at a lower altitude than observed as was found from the simulations performed during PECAN. Three of the four PBL schemes behaved similarly, but one, the ACM2, was often an outlier, failing to indicate the convective initiation.

Potential Vorticity Analysis of Low Level Thunderstorm Dynamics in an Idealized Supercell Simulation

DTIC Science & Technology

2009-03-01

Severe Weather, Supercell, Weather Research and Forecasting Model , Advanced WRF 16. PRICE CODE 17. SECURITY CLASSIFICATION OF REPORT...27 A. ADVANCED RESEARCH WRF MODEL .................................................27 1. Data, Model Setup, and Methodology...03/11/2006 GFS model run. Top row: 11/12Z initialization. Middle row: 12 hour forecast valid at 12/00Z. Bottom row: 24 hour forecast valid at

Toward Improved Land Surface Initialization in Support of Regional WRF Forecasts at the Kenya Meteorological Service (KMS)

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; Mungai, John; Sakwa, Vincent; Kabuchanga, Eric; Zavodsky, Bradley T.; Limaye, Ashutosh S.

2014-01-01

SPoRT/SERVIR/RCMRD/KMS Collaboration: Builds off strengths of each organization. SPoRT: Transition of satellite, modeling and verification capabilities; SERVIR-Africa/RCMRD: International capacity-building expertise; KMS: Operational organization with regional weather forecasting expertise in East Africa. Hypothesis: Improved land-surface initialization over Eastern Africa can lead to better temperature, moisture, and ultimately precipitation forecasts in NWP models. KMS currently initializes Weather Research and Forecasting (WRF) model with NCEP/Global Forecast System (GFS) model 0.5-deg initial / boundary condition data. LIS will provide much higher-resolution land-surface data at a scale more representative to regional WRF configuration. Future implementation of real-time NESDIS/VIIRS vegetation fraction to further improve land surface representativeness.

The influences of Wildfires and Stratospheric-Tropospheric exchange on ozone during SEACIONS mission over St. Louis, MO

NASA Astrophysics Data System (ADS)

Wilkins, J. L.

2015-12-01

A series of 32 ozonesondes were launched from St. Louis, Missouri, from 8 Aug - 23 Sept 2013, as part of the SouthEast American Consortium for Intensive Ozone Network Study (SEACIONS) mission. The time during which this site operated coincided with two large wildfires, Idaho's Beaver Creek fire and California's RIM fire, in addition to widespread agricultural fires in the Midwest. As part of our analyses, we examined multiple satellite-derived products that have been used in the analysis of tropospheric pollution, fires, and air mass flow patterns. The Fire Locating and Modeling of Burning Emissions (FLAMBE) inventory was used as an input to FLEXPART-WRF to quantify the contribution of particle trajectories and injection heights from the various sources. Trajectories from the sonde launch sites and fire locations were used as input for the two FLEXPART-WRF Model simulations to determine the origins of pollution plumes. The first simulation was conducted to model fire emissions within the planetary boundary layer (<3500m), while the second was added to investigate transportation effects from locations identified to have pyro-convective cumulonimbus. The Goddard Earth Observing System Model, Version 5 (GEOS-5) potential vorticity was used to analyze the stratospheric component of ozone enhancements. We examined three meteorological test cases: 1) a cut-off low, 2) a blocking high pressure, and 3) a frontal passage, which involve mixed-layer O3 enhancements, which can be spotted at several sites within SEACIONS. We look to quantify the contribution of these ozone enhancement sources to local air quality.

Assessment of the Suitability of High Resolution Numerical Weather Model Outputs for Hydrological Modelling in Mountainous Cold Regions

NASA Astrophysics Data System (ADS)

Rasouli, K.; Pomeroy, J. W.; Hayashi, M.; Fang, X.; Gutmann, E. D.; Li, Y.

2017-12-01

The hydrology of mountainous cold regions has a large spatial variability that is driven both by climate variability and near-surface process variability associated with complex terrain and patterns of vegetation, soils, and hydrogeology. There is a need to downscale large-scale atmospheric circulations towards the fine scales that cold regions hydrological processes operate at to assess their spatial variability in complex terrain and quantify uncertainties by comparison to field observations. In this research, three high resolution numerical weather prediction models, namely, the Intermediate Complexity Atmosphere Research (ICAR), Weather Research and Forecasting (WRF), and Global Environmental Multiscale (GEM) models are used to represent spatial and temporal patterns of atmospheric conditions appropriate for hydrological modelling. An area covering high mountains and foothills of the Canadian Rockies was selected to assess and compare high resolution ICAR (1 km × 1 km), WRF (4 km × 4 km), and GEM (2.5 km × 2.5 km) model outputs with station-based meteorological measurements. ICAR with very low computational cost was run with different initial and boundary conditions and with finer spatial resolution, which allowed an assessment of modelling uncertainty and scaling that was difficult with WRF. Results show that ICAR, when compared with WRF and GEM, performs very well in precipitation and air temperature modelling in the Canadian Rockies, while all three models show a fair performance in simulating wind and humidity fields. Representation of local-scale atmospheric dynamics leading to realistic fields of temperature and precipitation by ICAR, WRF, and GEM makes these models suitable for high resolution cold regions hydrological predictions in complex terrain, which is a key factor in estimating water security in western Canada.

A Comparison Of Primitive Model Results Of The Short Term Wind Energy Prediction System (Sweps): WRF vs MM5

NASA Astrophysics Data System (ADS)

Unal, E.; Tan, E.; Mentes, S. S.; Caglar, F.; Turkmen, M.; Unal, Y. S.; Onol, B.; Ozdemir, E. T.

2012-04-01

Although discontinuous behavior of wind field makes energy production more difficult, wind energy is the fastest growing renewable energy sector in Turkey which is the 6th largest electricity market in Europe. Short-term prediction systems, which capture the dynamical and statistical nature of the wind field in spatial and time scales, need to be advanced in order to increase the wind power prediction accuracy by using appropriate numerical weather forecast models. Therefore, in this study, performances of the next generation mesoscale Numerical Weather Forecasting model, WRF, and The Fifth-Generation NCAR/Penn State Mesoscale Model, MM5, have been compared for the Western Part of Turkey. MM5 has been widely used by Turkish State Meteorological Service from which MM5 results were also obtained. Two wind farms of the West Turkey have been analyzed for the model comparisons by using two different model domain structures. Each model domain has been constructed by 3 nested domains downscaling from 9km to 1km resolution by the ratio of 3. Since WRF and MM5 models have no exactly common boundary layer, cumulus, and microphysics schemes, the similar physics schemes have been chosen for these two models in order to have reasonable comparisons. The preliminary results show us that, depending on the location of the wind farms, MM5 wind speed RMSE values are 1 to 2 m/s greater than that of WRF values. Since 1 to 2 m/s errors can be amplified when wind speed is converted to wind power; it is decided that the WRF model results are going to be used for the rest of the project.

Characterising Brazilian biomass burning emissions using WRF-Chem with MOSAIC sectional aerosol

NASA Astrophysics Data System (ADS)

Archer-Nicholls, S.; Lowe, D.; Darbyshire, E.; Morgan, W. T.; Bela, M. M.; Pereira, G.; Trembath, J.; Kaiser, J. W.; Longo, K. M.; Freitas, S. R.; Coe, H.; McFiggans, G.

2014-09-01

The South American Biomass Burning Analysis (SAMBBA) field campaign took detailed in-situ flight measurements of aerosol during the 2012 dry season to characterise biomass burning aerosol and improve understanding of its impacts on weather and climate. Developments have been made to the Weather research and Forecast model with chemistry (WRF-Chem) model to improve the representation of biomass burning aerosol in the region by coupling a sectional aerosol scheme to the plume rise parameterisation. Brazilian Biomass Burning Emissions Model (3BEM) fire emissions are used, prepared using PREP-CHEM-SRC, and mapped to CBM-Z and MOSAIC species. Model results have been evaluated against remote sensing products, AERONET sites, and four case studies of flight measurements from the SAMBBA campaign. WRF-Chem predicted layers of elevated aerosol loadings (5-20 μg sm-3) of particulate organic matter at high altitude (6-8 km) over tropical forest regions, while flight measurements showed a sharp decrease above 2-4 km altitude. This difference was attributed to the plume-rise parameterisation overestimating injection height. The 3BEM emissions product was modified using estimates of active fire size and burned area for the 2012 fire season, which reduced the fire size. The enhancement factor for fire emissions was increased from 1.3 to 5 to retain reasonable aerosol optical depths (AOD). The smaller fire size lowered the injection height of the emissions, but WRF-Chem still showed elevated aerosol loadings between 4-5 km altitude. Over eastern Cerrado (savannah-like) regions, both modelled and measured aerosol loadings decreased above approximately 4 km altitude. Compared with MODIS satellite data and AERONET sites, WRF-Chem represented AOD magnitude well (between 0.3-1.5) over western tropical forest fire regions in the first half of the campaign, but tended to over-predict them in the second half, when precipitation was more significant. Over eastern Cerrado regions, WRF-Chem tended to under-predict AOD. Modeled aerosol loadings in the east were higher in the modified emission scenario. The primary organic matter to black carbon ratio was typically between 8-10 in WRF-Chem. This was lower than western flights measurements (interquartile range of 11.6-15.7 in B734, 14.7-24.0 in B739), but similar to the eastern flight B742 (8.1-10.4). However, single scattering albedo was close to measured over the western flights (0.87-0.89 in model; 0.88-0.91 in flight B734, and 0.86-0.95 in flight B739 measurements) but too high over the eastern flight B742 (0.86-0.87 in model, 0.81-0.84 in measurements). This suggests that improvements are needed to both modeled aerosol composition and optical properties calculations in WRF-Chem.

High resolution regional climate simulation of the Hawaiian Islands - Validation of the historical run from 2003 to 2012

NASA Astrophysics Data System (ADS)

Xue, L.; Newman, A. J.; Ikeda, K.; Rasmussen, R.; Clark, M. P.; Monaghan, A. J.

2016-12-01

A high-resolution (a 1.5 km grid spacing domain nested within a 4.5 km grid spacing domain) 10-year regional climate simulation over the entire Hawaiian archipelago is being conducted at the National Center for Atmospheric Research (NCAR) using the Weather Research and Forecasting (WRF) model version 3.7.1. Numerical sensitivity simulations of the Hawaiian Rainband Project (HaRP, a filed experiment from July to August in 1990) showed that the simulated precipitation properties are sensitive to initial and lateral boundary conditions, sea surface temperature (SST), land surface models, vertical resolution and cloud droplet concentration. The validations of model simulated statistics of the trade wind inversion, temperature, wind field, cloud cover, and precipitation over the islands against various observations from soundings, satellites, weather stations and rain gauges during the period from 2003 to 2012 will be presented at the meeting.

Evaluation of dynamically downscaled extreme temperature using a spatially-aggregated generalized extreme value (GEV) model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Jiali; Han, Yuefeng; Stein, Michael L.

2016-02-10

The Weather Research and Forecast (WRF) model downscaling skill in extreme maximum daily temperature is evaluated by using the generalized extreme value (GEV) distribution. While the GEV distribution has been used extensively in climatology and meteorology for estimating probabilities of extreme events, accurately estimating GEV parameters based on data from a single pixel can be difficult, even with fairly long data records. This work proposes a simple method assuming that the shape parameter, the most difficult of the three parameters to estimate, does not vary over a relatively large region. This approach is applied to evaluate 31-year WRF-downscaled extreme maximummore » temperature through comparison with North American Regional Reanalysis (NARR) data. Uncertainty in GEV parameter estimates and the statistical significance in the differences of estimates between WRF and NARR are accounted for by conducting bootstrap resampling. Despite certain biases over parts of the United States, overall, WRF shows good agreement with NARR in the spatial pattern and magnitudes of GEV parameter estimates. Both WRF and NARR show a significant increase in extreme maximum temperature over the southern Great Plains and southeastern United States in January and over the western United States in July. The GEV model shows clear benefits from the regionally constant shape parameter assumption, for example, leading to estimates of the location and scale parameters of the model that show coherent spatial patterns.« less

Development of WRF-ROI system by incorporating eigen-decomposition

NASA Astrophysics Data System (ADS)

Kim, S.; Noh, N.; Song, H.; Lim, G.

2011-12-01

This study presents the development of WRF-ROI system, which is the implementation of Retrospective Optimal Interpolation (ROI) to the Weather Research and Forecasting model (WRF). ROI is a new data assimilation algorithm introduced by Song et al. (2009) and Song and Lim (2009). The formulation of ROI is similar with that of Optimal Interpolation (OI), but ROI iteratively assimilates an observation set at a post analysis time into a prior analysis, possibly providing the high quality reanalysis data. ROI method assimilates the data at post analysis time using perturbation method (Errico and Raeder, 1999) without adjoint model. In previous study, ROI method is applied to Lorenz 40-variable model (Lorenz, 1996) to validate the algorithm and to investigate the capability. It is therefore required to apply this ROI method into a more realistic and complicated model framework such as WRF. In this research, the reduced-rank formulation of ROI is used instead of a reduced-resolution method. The computational costs can be reduced due to the eigen-decomposition of background error covariance in the reduced-rank method. When single profile of observations is assimilated in the WRF-ROI system by incorporating eigen-decomposition, the analysis error tends to be reduced if compared with the background error. The difference between forecast errors with assimilation and without assimilation is obviously increased as time passed, which means the improvement of forecast error by assimilation.

Dynamic Evaluation of Two Decades of WRF-CMAQ Ozone Simulations over the Contiguous United States (2017 CMAS)

EPA Science Inventory

Weather Research and Forecasting (WRF)–Community Multi-scale Air Quality (CMAQ) model over the contiguous United States is conducted to assess how well the changes in observed ozone air quality are simulated by the model. The changes induced by variations in meteorology and...

Predictability and Coupled Dynamics of MJO During DYNAMO

DTIC Science & Technology

2015-02-03

with two complementary atmosphere-only simulations with modified SST conditions. One WRF simulation is forced with the persistent initial SST, lacking...we have contributed to the following subset of accomplishments of the muhi-institutional team: a. Run SC0AR2 ( WRF -ROMS) in downscaling mode for the 2...Regional (SCOAR) Model Seo et al. (2007; 2014, J. Climate), http://scoar.wlklspaces.cotn p^ WRF /RSM C^ ROMS {j^TWo-way coupling ^ One

Impact of spectral nudging on regional climate simulation over CORDEX East Asia using WRF

NASA Astrophysics Data System (ADS)

Tang, Jianping; Wang, Shuyu; Niu, Xiaorui; Hui, Pinhong; Zong, Peishu; Wang, Xueyuan

2017-04-01

In this study, the impact of the spectral nudging method on regional climate simulation over the Coordinated Regional Climate Downscaling Experiment East Asia (CORDEX-EA) region is investigated using the Weather Research and Forecasting model (WRF). Driven by the ERA-Interim reanalysis, five continuous simulations covering 1989-2007 are conducted by the WRF model, in which four runs adopt the interior spectral nudging with different wavenumbers, nudging variables and nudging coefficients. Model validation shows that WRF has the ability to simulate spatial distributions and temporal variations of the surface climate (air temperature and precipitation) over CORDEX-EA domain. Comparably the spectral nudging technique is effective in improving the model's skill in the following aspects: (1), the simulated biases and root mean square errors of annual mean temperature and precipitation are obviously reduced. The SN3-UVT (spectral nudging with wavenumber 3 in both zonal and meridional directions applied to U, V and T) and SN6 (spectral nudging with wavenumber 6 in both zonal and meridional directions applied to U and V) experiments give the best simulations for temperature and precipitation respectively. The inter-annual and seasonal variances produced by the SN experiments are also closer to the ERA-Interim observation. (2), the application of spectral nudging in WRF is helpful for simulating the extreme temperature and precipitation, and the SN3-UVT simulation shows a clear advantage over the other simulations in depicting both the spatial distributions and inter-annual variances of temperature and precipitation extremes. With the spectral nudging, WRF is able to preserve the variability in the large scale climate information, and therefore adjust the temperature and precipitation variabilities toward the observation.

The 20-22 January 2007 Snow Events over Canada: Microphysical Properties

NASA Technical Reports Server (NTRS)

Tao. W.K.; Shi, J.J.; Matsui, T.; Hao, A.; Lang, S.; Peters-Lidard, C.; Skofronick-Jackson, G.; Petersen, W.; Cifelli, R.; Rutledge, S.

2009-01-01

One of the grand challenges of the Global Precipitation Measurement (GPM) mission is to improve precipitation measurements in mid- and high-latitudes during cold seasons through the use of high-frequency passive microwave radiometry. Toward this end, the Weather Research and Forecasting (WRF) model with the Goddard microphysics scheme is coupled with a Satellite Data Simulation Unit (WRF-SDSU) that has been developed to facilitate over-land snowfall retrieval algorithms by providing a virtual cloud library and microwave brightness temperature (Tb) measurements consistent with the GPM Microwave Imager (GMI). This study tested the Goddard cloud microphysics scheme in WRF for snowstorm events (January 20-22, 2007) that took place over the Canadian CloudSAT/CALIPSO Validation Project (C3VP) ground site (Centre for Atmospheric Research Experiments - CARE) in Ontario, Canada. In this paper, the performance of the Goddard cloud microphysics scheme both with 2ice (ice and snow) and 3ice (ice, snow and graupel) as well as other WRF microphysics schemes will be presented. The results are compared with data from the Environment Canada (EC) King Radar, an operational C-band radar located near the CARE site. In addition, the WRF model output is used to drive the Goddard SDSU to calculate radiances and backscattering signals consistent with direct satellite observations for evaluating the model results.

Enhancements to the WRF-Hydro Hydrologic Model Structure for Semi-arid Environments

NASA Astrophysics Data System (ADS)

Lahmers, T. M.; Gupta, H.; Hazenberg, P.; Castro, C. L.; Gochis, D.; Yates, D. N.; Dugger, A. L.; Goodrich, D. C.

2017-12-01

The NOAA National Water Center (NWC) implemented an operational National Water Model (NWM) in August 2016 to simulate and forecast streamflow and soil moisture throughout the Contiguous US (CONUS). The NWM is based on the WRF-Hydro hydrologic model architecture, with a 1-km resolution Noah-MP LSM grid and a 250m routing grid. The operational NWM does not currently resolve infiltration of water from the beds of ephemeral channels, which is an important component of the water balance in semi-arid environments common in many portions of the western US. This work demonstrates the benefit of a conceptual channel infiltration function in the WRF-Hydro model architecture following calibration. The updated model structure and parameters for the NWM architecture, when implemented operationally, will permit its use in flow simulation and forecasting in the southwest US, particularly for flash floods in basins with smaller drainage areas. Our channel infiltration function is based on that of the KINEROS2 semi-distributed hydrologic model, which has been tested throughout the southwest CONUS for flash flood forecasts. Model calibration utilizes the Dynamically Dimensioned Search (DDS) algorithm, and the model is calibrated using NLDAS-2 atmospheric forcing and NCEP Stage-IV precipitation. Our results show that adding channel infiltration to WRF-Hydro can produce a physically consistent hydrologic response with a high-resolution gauge based precipitation forcing dataset in the USDA-ARS Walnut Gulch Experimental Watershed. NWM WRF-Hydro is also tested for the Babocomari River, Beaver Creek, and Sycamore Creek catchments in southern and central Arizona. In these basins, model skill is degraded due to uncertainties in the NCEP Stage-IV precipitation forcing dataset.

Sensitivity of the weather research and forecasting model to parameterization schemes for regional climate of Nile River Basin

NASA Astrophysics Data System (ADS)

Tariku, Tebikachew Betru; Gan, Thian Yew

2018-06-01

Regional climate models (RCMs) have been used to simulate rainfall at relatively high spatial and temporal resolutions useful for sustainable water resources planning, design and management. In this study, the sensitivity of the RCM, weather research and forecasting (WRF), in modeling the regional climate of the Nile River Basin (NRB) was investigated using 31 combinations of different physical parameterization schemes which include cumulus (Cu), microphysics (MP), planetary boundary layer (PBL), land-surface model (LSM) and radiation (Ra) schemes. Using the European Centre for Medium-Range Weather Forecast (ECMWF) ERA-Interim reanalysis data as initial and lateral boundary conditions, WRF was configured to model the climate of NRB at a resolution of 36 km with 30 vertical levels. The 1999-2001 simulations using WRF were compared with satellite data combined with ground observation and the NCEP reanalysis data for 2 m surface air temperature (T2), rainfall, short- and longwave downward radiation at the surface (SWRAD, LWRAD). Overall, WRF simulated more accurate T2 and LWRAD (with correlation coefficients >0.8 and low root-mean-square error) than SWRAD and rainfall for the NRB. Further, the simulation of rainfall is more sensitive to PBL, Cu and MP schemes than other schemes of WRF. For example, WRF simulated less biased rainfall with Kain-Fritsch combined with MYJ than with YSU as the PBL scheme. The simulation of T2 is more sensitive to LSM and Ra than to Cu, PBL and MP schemes selected, SWRAD is more sensitive to MP and Ra than to Cu, LSM and PBL schemes, and LWRAD is more sensitive to LSM, Ra and PBL than Cu, and MP schemes. In summary, the following combination of schemes simulated the most representative regional climate of NRB: WSM3 microphysics, KF cumulus, MYJ PBL, RRTM longwave radiation and Dudhia shortwave radiation schemes, and Noah LSM. The above configuration of WRF coupled to the Noah LSM has also been shown to simulate representative regional climate of NRB over 1980-2001 which include a combination of wet and dry years of the NRB.

Sensitivity of the weather research and forecasting model to parameterization schemes for regional climate of Nile River Basin

NASA Astrophysics Data System (ADS)

Tariku, Tebikachew Betru; Gan, Thian Yew

2017-08-01

Regional climate models (RCMs) have been used to simulate rainfall at relatively high spatial and temporal resolutions useful for sustainable water resources planning, design and management. In this study, the sensitivity of the RCM, weather research and forecasting (WRF), in modeling the regional climate of the Nile River Basin (NRB) was investigated using 31 combinations of different physical parameterization schemes which include cumulus (Cu), microphysics (MP), planetary boundary layer (PBL), land-surface model (LSM) and radiation (Ra) schemes. Using the European Centre for Medium-Range Weather Forecast (ECMWF) ERA-Interim reanalysis data as initial and lateral boundary conditions, WRF was configured to model the climate of NRB at a resolution of 36 km with 30 vertical levels. The 1999-2001 simulations using WRF were compared with satellite data combined with ground observation and the NCEP reanalysis data for 2 m surface air temperature (T2), rainfall, short- and longwave downward radiation at the surface (SWRAD, LWRAD). Overall, WRF simulated more accurate T2 and LWRAD (with correlation coefficients >0.8 and low root-mean-square error) than SWRAD and rainfall for the NRB. Further, the simulation of rainfall is more sensitive to PBL, Cu and MP schemes than other schemes of WRF. For example, WRF simulated less biased rainfall with Kain-Fritsch combined with MYJ than with YSU as the PBL scheme. The simulation of T2 is more sensitive to LSM and Ra than to Cu, PBL and MP schemes selected, SWRAD is more sensitive to MP and Ra than to Cu, LSM and PBL schemes, and LWRAD is more sensitive to LSM, Ra and PBL than Cu, and MP schemes. In summary, the following combination of schemes simulated the most representative regional climate of NRB: WSM3 microphysics, KF cumulus, MYJ PBL, RRTM longwave radiation and Dudhia shortwave radiation schemes, and Noah LSM. The above configuration of WRF coupled to the Noah LSM has also been shown to simulate representative regional climate of NRB over 1980-2001 which include a combination of wet and dry years of the NRB.

Effects of Implementing Subgrid-Scale Cloud-Radiation Interactions in a Regional Climate Model

NASA Astrophysics Data System (ADS)

Herwehe, J. A.; Alapaty, K.; Otte, T.; Nolte, C. G.

2012-12-01

Interactions between atmospheric radiation, clouds, and aerosols are the most important processes that determine the climate and its variability. In regional scale models, when used at relatively coarse spatial resolutions (e.g., larger than 1 km), convective cumulus clouds need to be parameterized as subgrid-scale clouds. Like many groups, our regional climate modeling group at the EPA uses the Weather Research & Forecasting model (WRF) as a regional climate model (RCM). One of the findings from our RCM studies is that the summertime convective systems simulated by the WRF model are highly energetic, leading to excessive surface precipitation. We also found that the WRF model does not consider the interactions between convective clouds and radiation, thereby omitting an important process that drives the climate. Thus, the subgrid-scale cloudiness associated with convective clouds (from shallow cumuli to thunderstorms) does not exist and radiation passes through the atmosphere nearly unimpeded, potentially leading to overly energetic convection. This also has implications for air quality modeling systems that are dependent upon cloud properties from the WRF model, as the failure to account for subgrid-scale cloudiness can lead to problems such as the underrepresentation of aqueous chemistry processes within clouds and the overprediction of ozone from overactive photolysis. In an effort to advance the climate science of the cloud-aerosol-radiation (CAR) interactions in RCM systems, as a first step we have focused on linking the cumulus clouds with the radiation processes. To this end, our research group has implemented into WRF's Kain-Fritsch (KF) cumulus parameterization a cloudiness formulation that is widely used in global earth system models (e.g., CESM/CAM5). Estimated grid-scale cloudiness and associated condensate are adjusted to account for the subgrid clouds and then passed to WRF's Rapid Radiative Transfer Model - Global (RRTMG) radiation schemes to affect the shortwave and longwave radiative processes. To evaluate the effects of implementing the subgrid-scale cloud-radiation interactions on WRF regional climate simulations, a three-year study period (1988-1990) was simulated over the CONUS using two-way nested domains with 108 km and 36 km horizontal grid spacing, without and with the cumulus feedbacks to radiation, and without and with some form of four dimensional data assimilation (FDDA). Initial and lateral boundary conditions (as well as data for the FDDA, when enabled) were supplied from downscaled NCEP-NCAR Reanalysis II (R2) data sets. Evaluation of the simulation results will be presented comparing regional surface precipitation and temperature statistics with North American Regional Reanalysis (NARR) data and Climate Forecast System Reanalysis (CFSR) data, respectively, as well as comparison with available surface radiation (SURFRAD) and satellite (CERES) observations. This research supports improvements in the EPA's WRF-CMAQ modeling system, leading to better predictions of present and future air quality and climate interactions in order to protect human health and the environment.

An Integrated High Resolution Hydrometeorological Modeling Testbed using LIS and WRF

NASA Technical Reports Server (NTRS)

Kumar, Sujay V.; Peters-Lidard, Christa D.; Eastman, Joseph L.; Tao, Wei-Kuo

2007-01-01

Scientists have made great strides in modeling physical processes that represent various weather and climate phenomena. Many modeling systems that represent the major earth system components (the atmosphere, land surface, and ocean) have been developed over the years. However, developing advanced Earth system applications that integrates these independently developed modeling systems have remained a daunting task due to limitations in computer hardware and software. Recently, efforts such as the Earth System Modeling Ramework (ESMF) and Assistance for Land Modeling Activities (ALMA) have focused on developing standards, guidelines, and computational support for coupling earth system model components. In this article, the development of a coupled land-atmosphere hydrometeorological modeling system that adopts these community interoperability standards, is described. The land component is represented by the Land Information System (LIS), developed by scientists at the NASA Goddard Space Flight Center. The Weather Research and Forecasting (WRF) model, a mesoscale numerical weather prediction system, is used as the atmospheric component. LIS includes several community land surface models that can be executed at spatial scales as fine as 1km. The data management capabilities in LIS enable the direct use of high resolution satellite and observation data for modeling. Similarly, WRF includes several parameterizations and schemes for modeling radiation, microphysics, PBL and other processes. Thus the integrated LIS-WRF system facilitates several multi-model studies of land-atmosphere coupling that can be used to advance earth system studies.

Air quality modeling for the urban Jackson, Mississippi Region using a high resolution WRF/Chem model.

PubMed

Yerramilli, Anjaneyulu; Dodla, Venkata B; Desamsetti, Srinivas; Challa, Srinivas V; Young, John H; Patrick, Chuck; Baham, Julius M; Hughes, Robert L; Yerramilli, Sudha; Tuluri, Francis; Hardy, Mark G; Swanier, Shelton J

2011-06-01

In this study, an attempt was made to simulate the air quality with reference to ozone over the Jackson (Mississippi) region using an online WRF/Chem (Weather Research and Forecasting-Chemistry) model. The WRF/Chem model has the advantages of the integration of the meteorological and chemistry modules with the same computational grid and same physical parameterizations and includes the feedback between the atmospheric chemistry and physical processes. The model was designed to have three nested domains with the inner-most domain covering the study region with a resolution of 1 km. The model was integrated for 48 hours continuously starting from 0000 UTC of 6 June 2006 and the evolution of surface ozone and other precursor pollutants were analyzed. The model simulated atmospheric flow fields and distributions of NO2 and O3 were evaluated for each of the three different time periods. The GIS based spatial distribution maps for ozone, its precursors NO, NO2, CO and HONO and the back trajectories indicate that all the mobile sources in Jackson, Ridgeland and Madison contributing significantly for their formation. The present study demonstrates the applicability of WRF/Chem model to generate quantitative information at high spatial and temporal resolution for the development of decision support systems for air quality regulatory agencies and health administrators.

Role of upper-ocean on the intensity of Bay of Bengal cyclone `Phailin' as revealed by coupled simulation using Mesoscale Coupled Modeling System (WRF-ROMS)

NASA Astrophysics Data System (ADS)

Mani, B.; Mandal, M.

2016-12-01

Numerical prediction of tropical cyclone (TC) track has improved significantly in recent years, but not the intensity. It is well accepted that TC induced sea surface temperature (SST) cooling in conjunction with pre-existing upper-ocean features have major influences on tropical cyclone intensity. Absence of two-way atmosphere-ocean feedback in the stand-alone atmosphere models has major consequences on their prediction of TC intensity. The present study investigates the role of upper-ocean on prediction of TC intensity and track based on coupled and uncoupled simulation of the Bay of Bengal (BoB) cyclone `Phailin'. The coupled simulation is conducted with the Mesoscale Coupled Modeling System (MCMS) which is a fully coupled atmosphere-ocean modeling system that includes the non-hydrostatic atmospheric model (WRF-ARW) and the three-dimensional hydrostatic ocean model (ROMS). The uncoupled simulation is performed using the atmosphere component of MCMS i.e., the customized version of WRF-ARW for BoB cyclones with prescribed (RTG) SST. The track and intensity of the storm is significantly better simulated by the MCMS and closely followed the observation. The peak intensity, landfall position and time are accurately predicted by MCMS, whereas the uncoupled simulation over predicted the storm intensity. Validation of storm induced SST cooling with the merged microwave-infrared satellite SST indicates that the MCMS simulation shows better correlation both in terms of spatial spread of cold wake and its magnitude. The analysis also suggests that the Pre-existing Cyclonic Eddy (PCE) observed adjacent to the storm enhanced the TC induced SST cooling. It is observed that the response of SST (i.e., cooling) to storm intensity is 12hr with 95% statistical significance. The air-sea enthalpy flux shows a clear asymmetry between Front Left (FL) and Rear Right (RR) regime to the storm center where TC induced cooling is more than 0.5K/24hr. The analysis of atmospheric boundary layer reveals the formation of persistent stable boundary layer (SBL) over the cold wake, which caused asymmetry in TC structure by quelling convection in the rainbands downstream to the cold wake. The present study signifies the importance of using MCMS in prediction of the BoB cyclone and encourages further investigation with more cyclone cases.

The Impact of Microphysical Schemes on Hurricane Intensity and Track

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Shi, Jainn Jong; Chen, Shuyi S.; Lang, Stephen; Lin, Pay-Liam; Hong, Song-You; Peters-Lidard, Christa; Hou, Arthur

2011-01-01

During the past decade, both research and operational numerical weather prediction models [e.g. the Weather Research and Forecasting Model (WRF)] have started using more complex microphysical schemes originally developed for high-resolution cloud resolving models (CRMs) with 1-2 km or less horizontal resolutions. WRF is a next-generation meso-scale forecast model and assimilation system. It incorporates a modern software framework, advanced dynamics, numerics and data assimilation techniques, a multiple moveable nesting capability, and improved physical packages. WRF can be used for a wide range of applications, from idealized research to operational forecasting, with an emphasis on horizontal grid sizes in the range of 1-10 km. The current WRF includes several different microphysics options. At NASA Goddard, four different cloud microphysics options have been implemented into WRF. The performance of these schemes is compared to those of the other microphysics schemes available in WRF for an Atlantic hurricane case (Katrina). In addition, a brief review of previous modeling studies on the impact of microphysics schemes and processes on the intensity and track of hurricanes is presented and compared against the current Katrina study. In general, all of the studies show that microphysics schemes do not have a major impact on track forecasts but do have more of an effect on the simulated intensity. Also, nearly all of the previous studies found that simulated hurricanes had the strongest deepening or intensification when using only warm rain physics. This is because all of the simulated precipitating hydrometeors are large raindrops that quickly fall out near the eye-wall region, which would hydrostatically produce the lowest pressure. In addition, these studies suggested that intensities become unrealistically strong when evaporative cooling from cloud droplets and melting from ice particles are removed as this results in much weaker downdrafts in the simulated storms. However, there are many differences between the different modeling studies, which are identified and discussed.

Effects of Real-Time NASA Vegetation Data on Model Forecasts of Severe Weather

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; Bell, Jordan R.; LaFontaine, Frank J.; Peters-Lidard, Christa D.

2012-01-01

The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed a Greenness Vegetation Fraction (GVF) dataset, which is updated daily using swaths of Normalized Difference Vegetation Index data from the Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the NASA-EOS Aqua and Terra satellites. NASA SPoRT started generating daily real-time GVF composites at 1-km resolution over the Continental United States beginning 1 June 2010. A companion poster presentation (Bell et al.) primarily focuses on impact results in an offline configuration of the Noah land surface model (LSM) for the 2010 warm season, comparing the SPoRT/MODIS GVF dataset to the current operational monthly climatology GVF available within the National Centers for Environmental Prediction (NCEP) and Weather Research and Forecasting (WRF) models. This paper/presentation primarily focuses on individual case studies of severe weather events to determine the impacts and possible improvements by using the real-time, high-resolution SPoRT-MODIS GVFs in place of the coarser-resolution NCEP climatological GVFs in model simulations. The NASA-Unified WRF (NU-WRF) modeling system is employed to conduct the sensitivity simulations of individual events. The NU-WRF is an integrated modeling system based on the Advanced Research WRF dynamical core that is designed to represents aerosol, cloud, precipitation, and land processes at satellite-resolved scales in a coupled simulation environment. For this experiment, the coupling between the NASA Land Information System (LIS) and the WRF model is utilized to measure the impacts of the daily SPoRT/MODIS versus the monthly NCEP climatology GVFs. First, a spin-up run of the LIS is integrated for two years using the Noah LSM to ensure that the land surface fields reach an equilibrium state on the 4-km grid mesh used. Next, the spin-up LIS is run in two separate modes beginning on 1 June 2010, one continuing with the climatology GVFs while the other uses the daily SPoRT/MODIS GVFs. Finally, snapshots of the LIS land surface fields are used to initialize two different simulations of the NU-WRF, one running with climatology LIS and GVFs, and the other running with experimental LIS and NASA/SPoRT GVFs. In this paper/presentation, case study results will be highlighted in regions with significant differences in GVF between the NCEP climatology and SPoRT product during severe weather episodes.

Simulating seasonal tropical cyclone intensities at landfall along the South China coast

NASA Astrophysics Data System (ADS)

Lok, Charlie C. F.; Chan, Johnny C. L.

2018-04-01

A numerical method is developed using a regional climate model (RegCM3) and the Weather Forecast and Research (WRF) model to predict seasonal tropical cyclone (TC) intensities at landfall for the South China region. In designing the model system, three sensitivity tests have been performed to identify the optimal choice of the RegCM3 model domain, WRF horizontal resolution and WRF physics packages. Driven from the National Centers for Environmental Prediction Climate Forecast System Reanalysis dataset, the model system can produce a reasonable distribution of TC intensities at landfall on a seasonal scale. Analyses of the model output suggest that the strength and extent of the subtropical ridge in the East China Sea are crucial to simulating TC landfalls in the Guangdong and Hainan provinces. This study demonstrates the potential for predicting TC intensities at landfall on a seasonal basis as well as projecting future climate changes using numerical models.

Projecting water yield and ecosystem productivity across the United States by linking an ecohydrological model to WRF dynamically downscaled climate data

Treesearch

Shanlei Sun; Ge Sun; Erika Cohen Mack; Steve McNulty; Peter V. Caldwell; Kai Duan; Yang Zhang

2016-01-01

Quantifying the potential impacts of climatechange on water yield and ecosystem productivity is essential to developing sound watershed restoration plans, andecosystem adaptation and mitigation strategies. This study links an ecohydrological model (Water Supply and StressIndex, WaSSI) with WRF (Weather Research and Forecasting Model) using dynamically downscaled...

A Multiseason Comparison of the Forecast Skills among Three Numerical Models over Southcentral United States

NASA Astrophysics Data System (ADS)

Lu, D.; Reddy, S.

2005-05-01

During the summer 2003 and winter 2003-2004, three mesoscale numerical models, the fifth-generation Pennsylvania State University-NCAR Mesoscale Model (MM5), Navy's Coupled Ocean/Atmospheric Mesoscale Prediction System (COAMPS) and the Weather Research and Forecasting model (WRF), were operationally run at a horizontal resolution of 27 km twice daily in Jackson State University (JSU). Three models were run by the initial and lateral boundary conditions from AVN data. The purpose of this paper is to evaluate the performances of three models during these two seasons. It was found that the temporal variation of distribution and strength of mean error (ME) biases at 12, 24 and 36h was rather weak for surface temperature, sea level pressure and surface wind speed. During two seasons, the MM5 underpredicted the seasonal precipitation while the COAMPS and WRF overpredicted. This is consistent with the statistical score analyses of rainfall. The Bias scores revealed that the MM5 yielded an underprediction of precipitation, especially for heavier rainfall events. Due to the under estimate of rainfall areas and strength, the MM5 presented the lower TS, POD and KSS scores at lighter rainfall events compared to the COAMPS and WRF. At moderate to heavier thresholds, three models produced rather low KSS and POD scores that are consistent with the high FAR values. The WRF skills in predicting precipitation heavily depend on the performance of cumulus parameterization scheme. Instead of Kain-Fritsch scheme, using other two schemes, Grell-Devenyi and Bette-Miller-Janjic, in the WRF for warm season 2003 demonstrated that the precipitation overprediction had been efficiently suppressed. Overall, the performances of three models revealed that the best skill is at 12h and the worst at 36h.

Evaluation of the WRF model for precipitation downscaling on orographic complex islands

NASA Astrophysics Data System (ADS)

Díaz, Juan P.; González, Albano; Expósito, Francisco; Pérez, Juan C.

2010-05-01

General Circulation Models (GCMs) have proven to be an effective tool to simulate many aspects of large-scale and global climate. However, their applicability to climate impact studies is limited by their capabilities to resolve regional scale situations. In this sense, dynamical downscaling techniques are an appropriate alternative to estimate high resolution regional climatologies. In this work, the Weather Research and Forecasting model (WRF) has been used to simulate precipitations over the Canary Islands region during 2009. The precipitation patterns over Canary Islands, located at North Atlantic region, show large gradients over a relatively small geographical area due to large scale factors such as Trade Winds regime predominant in the area and mesoscale factors mainly due to the complex terrain. Sensitivity study of simulated WRF precipitations to variations in model setup and parameterizations was carried out. Thus, WRF experiments were performed using two way nesting at 3 km horizontal grid spacing and 28 vertical levels in the Canaries inner domain. The initial and lateral and lower boundary conditions for the outer domain were provided at 6 hourly intervals by NCEP FNL (Final) Operational Global Analysis data on 1.0x1.0 degree resolution interpolated onto the WRF model grid. Numerous model options have been tested, including different microphysics schemes, cumulus parameterizations and nudging configuration Positive-definite moisture advection condition was also checked. Two integration approaches were analyzed: a 1-year continuous long-term integration and a consecutive short-term monthly reinitialized integration. To assess the accuracy of our simulations, model results are compared against observational datasets obtained from a network of meteorological stations in the region. In general, we can observe that the regional model is able to reproduce the spatial distribution of precipitation, but overestimates rainfall, mainly during strong precipitation events.

Assessment of Wind Resource in the Palk Strait using Different Methods

NASA Astrophysics Data System (ADS)

Gupta, T.; Khan, F.; Baidya Roy, S.; Miller, L.

2017-12-01

The Government of India has proposed a target of 60 GW in grid power from the wind by the year 2022. The Palk Strait is one of the potential offshore wind power generation sites in India. It is a 65-135 km wide and 135 km long channel lying between the south eastern tip of India and northern Sri Lanka. The complex terrain bounding the two sides of the strait leads to enhanced wind speed and reduced variability in the wind direction. Here, we compare 3 distinct methodologies for estimating the generation rates for a hypothetical offshore wind farm array located in the strait. The methodologies include: 1) traditional wind power density model that ignores the effect of turbine interactions on generation rates; 2) the PARK wake model; and 3) a high resolution weather model (WRF) with a wind turbine parameterization. Using the WRF model as our baseline, we find that the simple model overestimates generation by an order-of-magnitude, while the wake model underestimates generation rates by about 5%. The reason for these differences relates to the influence of wind turbines on the atmospheric flow, wherein, the WRF model is able to capture the effect of both the complex terrain and wind turbine atmospheric boundary layer interactions. Lastly, a model evaluation is conducted which shows that 10m wind speeds and directions from WRF are comparable with the satellite data. Hence, we conclude from the study that each of these methodologies may have merit, but should a wind farm is deployed in such a complex terrain, we expect the WRF method to give better estimates of wind resource assessment capturing the physical processes emerging due to the interactions between offshore wind farm and the surrounding terrain.

Prediction of tropical cyclone over North Indian Ocean using WRF model: sensitivity to scatterometer winds, ATOVS and ATMS radiances

NASA Astrophysics Data System (ADS)

Dodla, Venkata B.; Srinivas, Desamsetti; Dasari, Hari Prasad; Gubbala, Chinna Satyanarayana

2016-05-01

Tropical cyclone prediction, in terms of intensification and movement, is important for disaster management and mitigation. Hitherto, research studies were focused on this issue that lead to improvement in numerical models, initial data with data assimilation, physical parameterizations and application of ensemble prediction. Weather Research and Forecasting (WRF) model is the state-of-art model for cyclone prediction. In the present study, prediction of tropical cyclone (Phailin, 2013) that formed in the North Indian Ocean (NIO) with and without data assimilation using WRF model has been made to assess impacts of data assimilation. WRF model was designed to have nested two domains of 15 and 5 km resolutions. In the present study, numerical experiments are made without and with the assimilation of scatterometer winds, and radiances from ATOVS and ATMS. The model performance was assessed in respect to the movement and intensification of cyclone. ATOVS data assimilation experiment had produced the best prediction with least errors less than 100 km up to 60 hours and producing pre-deepening and deepening periods accurately. The Control and SCAT wind assimilation experiments have shown good track but the errors were 150-200 km and gradual deepening from the beginning itself instead of sudden deepening.

A Real-Time MODIS Vegetation Composite for Land Surface Models and Short-Term Forecasting

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; LaFontaine, Frank J.; Kumar, Sujay V.; Jedlovec, Gary J.

2011-01-01

The NASA Short-term Prediction Research and Transition (SPoRT) Center is producing real-time, 1- km resolution Normalized Difference Vegetation Index (NDVI) gridded composites over a Continental U.S. domain. These composites are updated daily based on swath data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the polar orbiting NASA Aqua and Terra satellites, with a product time lag of about one day. A simple time-weighting algorithm is applied to the NDVI swath data that queries the previous 20 days of data to ensure a continuous grid of data populated at all pixels. The daily composites exhibited good continuity both spatially and temporally during June and July 2010. The composites also nicely depicted high greenness anomalies that resulted from significant rainfall over southwestern Texas, Mexico, and New Mexico during July due to early-season tropical cyclone activity. The SPoRT Center is in the process of computing greenness vegetation fraction (GVF) composites from the MODIS NDVI data at the same spatial and temporal resolution for use in the NASA Land Information System (LIS). The new daily GVF dataset would replace the monthly climatological GVF database (based on Advanced Very High Resolution Radiometer [AVHRR] observations from 1992-93) currently available to the Noah land surface model (LSM) in both LIS and the public version of the Weather Research and Forecasting (WRF) model. The much higher spatial resolution (1 km versus 0.15 degree) and daily updates based on real-time satellite observations have the capability to greatly improve the simulation of the surface energy budget in the Noah LSM within LIS and WRF. Once code is developed in LIS to incorporate the daily updated GVFs, the SPoRT Center will conduct simulation sensitivity experiments to quantify the impacts and improvements realized by the MODIS real-time GVF data. This presentation will describe the methodology used to develop the 1-km MODIS NDVI composites and show sample output from summer 2010, compare the MODIS GVF data to the AVHRR monthly climatology, and illustrate the sensitivity of the Noah LSM within LIS and/or the coupled LIS/WRF system to the new MODIS GVF dataset.

Climate Change in Small Islands

NASA Astrophysics Data System (ADS)

Tomé, Ricardo; Miranda, Pedro M. A.; Brito de Azevedo, Eduardo; Teixeira, Miguel A. C.

2014-05-01

Isolated islands are especially vulnerable to climate change. But their climate is generally not well reproduced in GCMs, due to their small size and complex topography. Here, results from a new generation of climate models, forced by scenarios RCP8.5 and RCP4.5 of greenhouse gases and atmospheric aerosol concentrations, established by the IPCC for its fifth report, are used to characterize the climate of the islands of Azores and Madeira, and its response to the ongoing global warming. The methodology developed here uses the new global model EC-Earth, data from ERA-Interim reanalysis and results from an extensive set of simulations with the WRF research model, using, for the first time, a dynamic approach for the regionalization of global fields at sufficiently fine resolutions, in which the effect of topographical complexity is explicitly represented. The results reviewed here suggest increases in temperature above 1C in the middle of the XXI century in Azores and Madeira, reaching values higher than 2.5C at the end of the century, accompanied by a reduction in the annual rainfall of around 10% in the Azores, which could reach 30% in Madeira. These changes are large enough to justify much broader impacts on island ecosystems and the human population. The results show the advantage of using the proposed methodology, in particular for an adequate representation of the precipitation regime in islands with complex topography, even suggesting the need for higher resolutions in future work. The WRF results are also compared against two different downscaling techniques using an air mass transformation model and a modified version of the upslope precipitation model of Smith and Barstad (2005).

Analysis and hindcast simulations of an extreme rainfall event in the Mediterranean area: The Genoa 2011 case

NASA Astrophysics Data System (ADS)

Fiori, E.; Comellas, A.; Molini, L.; Rebora, N.; Siccardi, F.; Gochis, D. J.; Tanelli, S.; Parodi, A.

2014-03-01

The city of Genoa, which places between the Tyrrhenian Sea and the Apennine mountains (Liguria, Italy) was rocked by severe flash floods on the 4th of November, 2011. Nearly 500 mm of rain, a third of the average annual rainfall, fell in six hours. Six people perished and millions of Euros in damages occurred. The synoptic-scale meteorological system moved across the Atlantic Ocean and into the Mediterranean generating floods that killed 5 people in Southern France, before moving over the Ligurian Sea and Genoa producing the extreme event studied here. Cloud-permitting simulations (1 km) of the finger-like convective system responsible for the torrential event over Genoa have been performed using Advanced Research Weather and Forecasting Model (ARW-WRF, version 3.3). Two different microphysics (WSM6 and Thompson) as well as three different convection closures (explicit, Kain-Fritsch, and Betts-Miller-Janjic) were evaluated to gain a deeper understanding of the physical processes underlying the observed heavy rain event and the model's capability to predict, in hindcast mode, its structure and evolution. The impact of forecast initialization and of model vertical discretization on hindcast results is also examined. Comparison between model hindcasts and observed fields provided by raingauge data, satellite data, and radar data show that this particular event is strongly sensitive to the details of the mesoscale initialization despite being evolved from a relatively large scale weather system. Only meso-γ details of the event were not well captured by the best setting of the ARW-WRF model and so peak hourly rainfalls were not exceptionally well reproduced. The results also show that specification of microphysical parameters suitable to these events have a positive impact on the prediction of heavy precipitation intensity values.

Comparisons of Anvil Cirrus Spatial Characteristics between Airborne Observations in DC3 Campaign and WRF Simulations

NASA Astrophysics Data System (ADS)

D'Alessandro, J.; Diao, M.; Chen, M.

2015-12-01

John D'Alessandro1, Minghui Diao1, Ming Chen2, George Bryan2, Hugh Morrison21. Department of Meteorology and Climate Science, San Jose State University2. Mesoscale & Microscale Meteorology Division, National Center for Atmospheric Research, Boulder, CO, 80301 Ice crystal formation requires the prerequisite condition of ice supersaturation, i.e., relative humidity with respect to ice (RHi) greater than 100%. The formation and evolution of ice supersaturated regions (ISSRs) has large impact on the subsequent formation of ice clouds. To examine the characteristics of simulated ice supersaturated regions at various model spatial resolutions, case studies between airborne in-situ measurements in the NSF Deep Convective, Clouds and Chemistry (DC3) campaign (May - June 2012) and WRF simulations are conducted in this work. Recent studies using ~200 m in-situ observations showed that ice supersaturated regions are mostly around 1 km in horizontal scale (Diao et al. 2014). Yet it is still unclear if such observed characteristics can be represented by WRF simulations at various spatial resolutions. In this work, we compare the WRF simulated anvil cirrus spatial characteristics with those observed in the DC3 campaign over the southern great plains in US. The WRF model is run at 1 km and 3 km horizontal grid spacing with a recent update of Thompson microphysics scheme. Our comparisons focus on the spatial characteristics of ISSRs and cirrus clouds, including the distributions of their horizontal scales, the maximum relative humidity with respect to ice (RHi) and the relationship between RHi and temperature. Our previous work on the NCAR CM1 cloud-resolving model shows that the higher resolution runs (i.e., 250m and 1km) generally have better agreement with observations than the coarser resolution (4km) runs. We will examine if similar trend exists for WRF simulations in deep convection cases. In addition, we will compare the simulation results between WRF and CM1, particularly for spatial correlations between ISSRs and cirrus and their evolution (based on the method of Diao et al. 2013). Overall, our work will help to assess the representation of ISSRs and cirrus in WRF simulation based on comparisons with in-situ observations.

Aerosol indirect effect on the grid-scale clouds in the two-way coupled WRF-CMAQ: model description, development, evaluation and regional analysis

EPA Science Inventory

This study implemented first, second and glaciations aerosol indirect effects (AIE) on resolved clouds in the two-way coupled WRF-CMAQ modeling system by including parameterizations for both cloud drop and ice number concentrations on the basis of CMAQ predicted aerosol distribu...

Grid-scale Indirect Radiative Forcing of Climate due to aerosols over the northern hemisphere simulated by the integrated WRF-CMAQ model: Preliminary results

EPA Science Inventory

In this study, indirect aerosol effects on grid-scale clouds were implemented in the integrated WRF3.3-CMAQ5.0 modeling system by including parameterizations for both cloud droplet and ice number concentrations calculated from the CMAQ-predicted aerosol particles. The resulting c...

A Public-Private-Acadmic Partnership to Advance Solar Power Forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Haupt, Sue Ellen

The National Center for Atmospheric Research (NCAR) is pleased to have led a partnership to advance the state-of-the-science of solar power forecasting by designing, developing, building, deploying, testing, and assessing the SunCast™ Solar Power Forecasting System. The project has included cutting edge research, testing in several geographically- and climatologically-diverse high penetration solar utilities and Independent System Operators (ISOs), and wide dissemination of the research results to raise the bar on solar power forecasting technology. The partners include three other national laboratories, six universities, and industry partners. This public-private-academic team has worked in concert to perform use-inspired research to advance solarmore » power forecasting through cutting-edge research to advance both the necessary forecasting technologies and the metrics for evaluating them. The project has culminated in a year-long, full-scale demonstration of provide irradiance and power forecasts to utilities and ISOs to use in their operations. The project focused on providing elements of a value chain, beginning with the weather that causes a deviation from clear sky irradiance and progresses through monitoring and observations, modeling, forecasting, dissemination and communication of the forecasts, interpretation of the forecast, and through decision-making, which produces outcomes that have an economic value. The system has been evaluated using metrics developed specifically for this project, which has provided rich information on model and system performance. Research was accomplished on the very short range (0-6 hours) Nowcasting system as well as on the longer term (6-72 hour) forecasting system. The shortest range forecasts are based on observations in the field. The shortest range system, built by Brookhaven National Laboratory (BNL) and based on Total Sky Imagers (TSIs) is TSICast, which operates on the shortest time scale with a latency of only a few minutes and forecasts that currently go out to about 15 min. This project has facilitated research in improving the hardware and software so that the new high definition cameras deployed at multiple nearby locations allow discernment of the clouds at varying levels and advection according to the winds observed at those levels. Improvements over “smart persistence” are about 29% for even these very short forecasts. StatCast is based on pyranometer data measured at the site as well as concurrent meteorological observations and forecasts. StatCast is based on regime-dependent artificial intelligence forecasting techniques and has been shown to improve on “smart persistence” forecasts by 15-50%. A second category of short-range forecasting systems employ satellite imagery and use that information to discern clouds and their motion, allowing them to project the clouds, and the resulting blockage of irradiance, in time. CIRACast (the system produced by the Cooperative Institute for Atmospheric Research [CIRA] at Colorado State University) was already one of the more advanced cloud motion systems, which is the reason that team was brought to this project. During the project timeframe, the CIRA team was able to advance cloud shadowing, parallax removal, and implementation of better advecting winds at different altitudes. CIRACast shows generally a 25-40% improvement over Smart Persistence between sunrise and approximately 1600 UTC (Coordinated Universal Time) . A second satellite-based system, MADCast (Multi-sensor Advective Diffusive foreCast system), assimilates data from multiple satellite imagers and profilers to assimilate a fully three-dimensional picture of the cloud into the dynamic core of WRF. During 2015, MADCast (provided at least 70% improvement over Smart Persistence, with most of that skill being derived during partly cloudy conditions. That allows advection of the clouds via the Weather Research and Forecasting (WRF) model dynamics directly. After WRF-Solar™ showed initial success, it was also deployed in nowcasting mode with coarser runs out to 6 hours made hourly. It provided improvements on the order of 50-60% over Smart Persistence for forecasts up to 1600 UTC. The advantages of WRF-Solar-Nowcasting and MADCast were then blended to develop the new MAD-WRF model that incorporates the most important features of each of those models, both assimilating satellite cloud fields and using WRF-So far physics to develop and dissipate clouds. MAE improvements for MAD-WRF for forecasts from 3-6 hours are improved over WRF-Solar-Now by 20%. While all the Nowcasting system components by themselves provide improvement over Smart Persistence, the largest benefit is derived when they are smartly blended together by the Nowcasting Integrator to produce an integrated forecast. The development of WRF-Solar™ under this project has provided the first numerical weather prediction (NWP) model specifically designed to meet the needs of irradiance forecasting. The first augmentation improved the solar tracking algorithm to account for deviations associated with the eccentricity of the Earth’s orbit and the obliquity of the Earth. Second, WRF-Solar™ added the direct normal irradiance (DNI) and diffuse (DIF) components from the radiation parameterization to the model output. Third, efficient parameterizations were implemented to either interpolate the irradiance in between calls to the expensive radiative transfer parameterization, or to use a fast radiative transfer code that avoids computing three-dimensional heating rates but provides the surface irradiance. Fourth, a new parameterization was developed to improve the representation of absorption and scattering of radiation by aerosols (aerosol direct effect). A fifth advance is that the aerosols now interact with the cloud microphysics, altering the cloud evolution and radiative properties, an effect that has been traditionally only implemented in atmospheric computationally costly chemistry models. A sixth development accounts for the feedbacks that sub-grid scale clouds produce in shortwave irradiance as implemented in a shallow cumulus parameterization Finally, WRF-Solar™ also allows assimilation of infrared irradiances from satellites to determine the three dimensional cloud field, allowing for an improved initialization of the cloud field that increases the performance of short-range forecasts. We find that WRF-Solar™ can improve clear sky irradiance prediction by 15-80% over a standard version of WRF, depending on location and cloud conditions. In a formal comparison to the NAM baseline, WRF-Solar™ showed improvements in the Day-Ahead forecast of 22-42%. The SunCast™ system requires substantial software engineering to blend all of the new model components as well as existing publically available NWP model runs. To do this we use an expert system for the Nowcasting blender and the Dynamic Integrated foreCast (DICast®) system for the NWP models. These two systems are then blended, we use an empirical power conversion method to convert the irradiance predictions to power, then apply an analog ensemble (AnEn) approach to further tune the forecast as well as to estimate its uncertainty. The AnEn module decreased RMSE (root mean squared error) by 17% over the blended SunCast™ power forecasts and provided skill in the probabilistic forecast with a Brier Skill Score of 0.55. In addition, we have also developed a Gridded Atmospheric Forecast System (GRAFS) in parallel, leveraging cost share funds. An economic evaluation based on Production Cost Modeling in the Public Service Company of Colorado showed that the observed 50% improvement in forecast accuracy will save their customers $819,200 with the projected MW deployment for 2024. Using econometrics, NCAR has scaled this savings to a national level and shown that an annual expected savings for this 50% forecast error reduction ranges from $11M in 2015 to $43M expected in 2040 with increased solar deployment. This amounts to a $455M discounted savings over the 26 year period of analysis.« less

Application of WRF/Chem-MADRID and WRF/Polyphemus in Europe - Part 1: Model description and evaluation of meteorological predictions

NASA Astrophysics Data System (ADS)

Zhang, Y.; Sartelet, K.; Wu, S.-Y.; Seigneur, C.

2013-02-01

Comprehensive model evaluation and comparison of two 3-D air quality modeling systems (i.e. the Weather Research and Forecast model (WRF)/Polyphemus and WRF with chemistry and the Model of Aerosol Dynamics, Reaction, Ionization, and Dissolution (MADRID) (WRF/Chem-MADRID) are conducted over western Europe. Part 1 describes the background information for the model comparison and simulation design, as well as the application of WRF for January and July 2001 over triple-nested domains in western Europe at three horizontal grid resolutions: 0.5°, 0.125°, and 0.025°. Six simulated meteorological variables (i.e. temperature at 2 m (T2), specific humidity at 2 m (Q2), relative humidity at 2 m (RH2), wind speed at 10 m (WS10), wind direction at 10 m (WD10), and precipitation (Precip)) are evaluated using available observations in terms of spatial distribution, domainwide daily and site-specific hourly variations, and domainwide performance statistics. WRF demonstrates its capability in capturing diurnal/seasonal variations and spatial gradients of major meteorological variables. While the domainwide performance of T2, Q2, RH2, and WD10 at all three grid resolutions is satisfactory overall, large positive or negative biases occur in WS10 and Precip even at 0.025°. In addition, discrepancies between simulations and observations exist in T2, Q2, WS10, and Precip at mountain/high altitude sites and large urban center sites in both months, in particular, during snow events or thunderstorms. These results indicate the model's difficulty in capturing meteorological variables in complex terrain and subgrid-scale meteorological phenomena, due to inaccuracies in model initialization parameterization (e.g. lack of soil temperature and moisture nudging), limitations in the physical parameterizations of the planetary boundary layer (e.g. cloud microphysics, cumulus parameterizations, and ice nucleation treatments) as well as limitations in surface heat and moisture budget parameterizations (e.g. snow-related processes, subgrid-scale surface roughness elements, and urban canopy/heat island treatments and CO2 domes). While the use of finer grid resolutions of 0.125° and 0.025° shows some improvement for WS10, Precip, and some mesoscale events (e.g. strong forced convection and heavy precipitation), it does not significantly improve the overall statistical performance for all meteorological variables except for Precip. These results indicate a need to further improve the model representations of the above parameterizations at all scales.

On the added value and sensitivity of WRF to driving conditions over CORDEX-Africa domain

NASA Astrophysics Data System (ADS)

Lorente-Plazas, Raquel; García-Díez, Markel; Jimenez-Guerrero, Pedro; Fernández, Jesús; Montavez, Juan Pedro

2014-05-01

The assessment of the climate variability over Africa has recently attracted the interest of the regional climate downscaling research community. The main reasons are not only because Africa is a climate change hot-spot, but also due to the low capacity of this region for the adaptation and mitigation under negative impacts and its direct dependency on its socio-economic sustainability of the climate variability. Therefore, improvements in the understanding of the African climate could help the governments in decision-making. Under this umbrella, regional climate models (RCMs) are promising tools to assess the African regional climate. The main advantage of the RCMs, with respect to global reanalysis datasets, is the higher detail provided by the increased resolution which implies a better representation of land-surface interactions and atmospheric processes. However, the confidence on the RCMs strongly depends on the reduction/bounding of uncertainties. One of these sources of uncertainties is associated with the selection of the boundary conditions for driving the regional models. In this work, two identical CORDEX-compliant simulations have been performed over Africa with the unique difference of being driven by two different reanalyses. The reanalyses used were the European Centre for Medium Range Weather Forecasts Interim reanalysis (ERA-I) and the Japanese 25-year reanalysis (JRA-25) by the Japanese Meteorological Service. Both reanalyses have identical temporal resolution (6-hr) but different spatial grid resolution, 0.75 and 1.25 degrees, respectively. The regional model used was the Weather Research and Forecasting Model (WRF). The numerical experiments encompass the period 1989-2010 covering the Africa-CORDEX domain with a 50 km horizontal spatial resolution and 28 vertical levels up to 50 hPa. The WRF simulations are compared between them and against observations. For the mean and maximum temperature the CRU monthly time series (0.25deg) from Climatic Research Unit of the University of East Anglia are used. The precipitation is compared against the Tropical Rainfall Measuring Mission Project (TRMM) monthly data (0.25deg). The results depict that none of the reanalyses used outperforms the other in representing the African climate, since their performance depends on the variable, season and region assessed. The simulations show a noticeable disagreement for 2-m temperature in north-western Africa, where WRF-JRA tends to underestimate this variable mostly in winter and spring. For the monthly mean daily maximum temperature, WRF-JRA tends to overestimate the temperature in the Sahel in summer and in the border between Angola and Namibia in Winter. When comparing with CRU observations, there is a remarkably better spatial representation for the WRF-EI simulation in the North of Africa. However, the behaviour of WRF-EI and WRF-JRA is similar in the South of Africa. Intra-annual variability is well represented except in Atlas mountains where WRF-JRA underestimates temperature. Regarding precipitation, the main differences appear over the Sahel region in JAS and in the Congo area during JFM. The comparison with the TRMM data shows a better agreement with the WRF-JRA simulation except during summer in the Sahel region. The monthly annual cycle is well captured, except in Ethiopian highlands and Northern West Africa where WRF-JRA (WRF-EI) underestimate (overestimate) the annual cycle.

A Coupled fcGCM-GCE Modeling System: A 3D Cloud Resolving Model and a Regional Scale Model

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo

2005-01-01

Recent GEWEX Cloud System Study (GCSS) model comparison projects have indicated that cloud-resolving models (CRMs) agree with observations better than traditional single-column models in simulating various types of clouds and cloud systems from different geographic locations. Current and future NASA satellite programs can provide cloud, precipitation, aerosol and other data at very fine spatial and temporal scales. It requires a coupled global circulation model (GCM) and cloud-scale model (termed a super-parameterization or multi-scale modeling framework, MMF) to use these satellite data to improve the understanding of the physical processes that are responsible for the variation in global and regional climate and hydrological systems. The use of a GCM will enable global coverage, and the use of a CRM will allow for better and ore sophisticated physical parameterization. NASA satellite and field campaign cloud related datasets can provide initial conditions as well as validation for both the MMF and CRMs. The Goddard MMF is based on the 2D Goddard Cumulus Ensemble (GCE) model and the Goddard finite volume general circulation model (fvGCM), and it has started production runs with two years results (1998 and 1999). Also, at Goddard, we have implemented several Goddard microphysical schemes (21CE, several 31CE), Goddard radiation (including explicity calculated cloud optical properties), and Goddard Land Information (LIS, that includes the CLM and NOAH land surface models) into a next generation regional scale model, WRF. In this talk, I will present: (1) A Brief review on GCE model and its applications on precipitation processes (microphysical and land processes), (2) The Goddard MMF and the major difference between two existing MMFs (CSU MMF and Goddard MMF), and preliminary results (the comparison with traditional GCMs), (3) A discussion on the Goddard WRF version (its developments and applications), and (4) The characteristics of the four-dimensional cloud data sets (or cloud library) stored at Goddard.

A comparison study of convective and microphysical parameterization schemes associated with lightning occurrence in southeastern Brazil using the WRF model

NASA Astrophysics Data System (ADS)

Zepka, G. D.; Pinto, O.

2010-12-01

The intent of this study is to identify the combination of convective and microphysical WRF parameterizations that better adjusts to lightning occurrence over southeastern Brazil. Twelve thunderstorm days were simulated with WRF model using three different convective parameterizations (Kain-Fritsch, Betts-Miller-Janjic and Grell-Devenyi ensemble) and two different microphysical schemes (Purdue-Lin and WSM6). In order to test the combinations of parameterizations at the same time of lightning occurrence, a comparison was made between the WRF grid point values of surface-based Convective Available Potential Energy (CAPE), Lifted Index (LI), K-Index (KI) and equivalent potential temperature (theta-e), and the lightning locations nearby those grid points. Histograms were built up to show the ratio of the occurrence of different values of these variables for WRF grid points associated with lightning to all WRF grid points. The first conclusion from this analysis was that the choice of microphysics did not change appreciably the results as much as different convective schemes. The Betts-Miller-Janjic parameterization has generally worst skill to relate higher magnitudes for all four variables to lightning occurrence. The differences between the Kain-Fritsch and Grell-Devenyi ensemble schemes were not large. This fact can be attributed to the similar main assumptions used by these schemes that consider entrainment/detrainment processes along the cloud boundaries. After that, we examined three case studies using the combinations of convective and microphysical options without the Betts-Miller-Janjic scheme. Differently from the traditional verification procedures, fields of surface-based CAPE from WRF 10 km domain were compared to the Eta model, satellite images and lightning data. In general the more reliable convective scheme was Kain-Fritsch since it provided more consistent distribution of the CAPE fields with respect to satellite images and lightning data.

The Impact of Ensemble Kalman Filter Assimilation of Near-Surface Observations on the Predictability of Atmospheric Conditions over Complex Terrain: Results from Recent MATERHORN Field Program

NASA Astrophysics Data System (ADS)

Pu, Z.; Zhang, H.

2013-12-01

Near-surface atmospheric observations are the main conventional observations for weather forecasts. However, in modern numerical weather prediction, the use of surface observations, especially those data over complex terrain, remains a unique challenge. There are fundamental difficulties in assimilating surface observations with three-dimensional variational data assimilation (3DVAR). In our early study[1] (Pu et al. 2013), a series of observing system simulation experiments was performed with the ensemble Kalman filter (EnKF) and compared with 3DVAR for its ability to assimilate surface observations with 3DVAR. Using the advanced research version of the Weather Research and Forecasting (WRF) model, results demonstrate that the EnKF can overcome some fundamental limitations that 3DVAR has in assimilating surface observations over complex terrain. Specifically, through its flow-dependent background error term, the EnKF produces more realistic analysis increments over complex terrain in general. Over complex terrain, the EnKF clearly performs better than 3DVAR, because it is more capable of handling surface data in the presence of terrain misrepresentation. With this presentation, we further examine the impact of EnKF data assimilation on the predictability of atmospheric conditions over complex terrain with the WRF model and the observations obtained from the most recent field experiments of the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) Program. The MATERHORN program provides comprehensive observations over mountainous regions, allowing the opportunity to study the predictability of atmospheric conditions over complex terrain in great details. Specifically, during fall 2012 and spring 2013, comprehensive observations were collected of soil states, surface energy budgets, near-surface atmospheric conditions, and profiling measurements from multiple platforms (e.g., balloon, lidar, radiosondes, etc.) over Dugway Proving Ground (DPG), Utah. With the near-surface observations and sounding data obtained during the MATERHORN fall 2012 field experiment, a month-long cycled EnKF analysis and forecast was produced with the WRF model and an advanced EnKF data assimilation system. Results are compared with the WRF near real-time forecasting during the same month and a set of analysis with 3DVAR data assimilation. Overall evaluation suggests some useful insights on the impacts of different data assimilation methods, surface and soil states, terrain representation on the predictability of atmospheric conditions over mountainous terrain. Details will be presented. References [1] Pu, Z., H. Zhang, and J. A. Anderson,. 'Ensemble Kalman filter assimilation of near-surface observations over complex terrain: Comparison with 3DVAR for short-range forecasts.' Tellus A, vol. 65,19620. 2013. http://dx.doi.org/10.3402/tellusa.v65i0. 19620.

Why is the simulated climatology of tropical cyclones so sensitive to the choice of cumulus parameterization scheme in the WRF model?

NASA Astrophysics Data System (ADS)

Zhang, Chunxi; Wang, Yuqing

2018-01-01

The sensitivity of simulated tropical cyclones (TCs) to the choice of cumulus parameterization (CP) scheme in the advanced Weather Research and Forecasting Model (WRF-ARW) version 3.5 is analyzed based on ten seasonal simulations with 20-km horizontal grid spacing over the western North Pacific. Results show that the simulated frequency and intensity of TCs are very sensitive to the choice of the CP scheme. The sensitivity can be explained well by the difference in the low-level circulation in a height and sorted moisture space. By transporting moist static energy from dry to moist region, the low-level circulation is important to convective self-aggregation which is believed to be related to genesis of TC-like vortices (TCLVs) and TCs in idealized settings. The radiative and evaporative cooling associated with low-level clouds and shallow convection in dry regions is found to play a crucial role in driving the moisture-sorted low-level circulation. With shallow convection turned off in a CP scheme, relatively strong precipitation occurs frequently in dry regions. In this case, the diabatic cooling can still drive the low-level circulation but its strength is reduced and thus TCLV/TC genesis is suppressed. The inclusion of the cumulus momentum transport (CMT) in a CP scheme can considerably suppress genesis of TCLVs/TCs, while changes in the moisture-sorted low-level circulation and horizontal distribution of precipitation are trivial, indicating that the CMT modulates the TCLVs/TCs activities in the model by mechanisms other than the horizontal transport of moist static energy.

A Coupled GCM-Cloud Resolving Modeling System, and a Regional Scale Model to Study Precipitation Processes

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo

2007-01-01

Recent GEWEX Cloud System Study (GCSS) model comparison projects have indicated that cloud-resolving models (CRMs) agree with observations better than traditional single-column models in simulating various types of clouds and cloud systems from different geographic locations. Current and future NASA satellite programs can provide cloud, precipitation, aerosol and other data at very fine spatial and temporal scales. It requires a coupled global circulation model (GCM) and cloud-scale model (termed a superparameterization or multi-scale modeling framework, MMF) to use these satellite data to improve the understanding of the physical processes that are responsible for the variation in global and regional climate and hydrological systems. The use of a GCM will enable global coverage, and the use of a CRM will allow for better and more sophisticated physical parameterization. NASA satellite and field campaign cloud related datasets can provide initial conditions as well as validation for both the MMF and CRMs. The Goddard MMF is based on the 2D Goddard Cumulus Ensemble (GCE) model and the Goddard finite volume general circulation model (fvGCM), and it has started production runs with two years results (1998 and 1999). Also, at Goddard, we have implemented several Goddard microphysical schemes (2ICE, several 31CE), Goddard radiation (including explicitly calculated cloud optical properties), and Goddard Land Information (LIS, that includes the CLM and NOAH land surface models) into a next generatio11 regional scale model, WRF. In this talk, I will present: (1) A brief review on GCE model and its applications on precipitation processes (microphysical and land processes), (2) The Goddard MMF and the major difference between two existing MMFs (CSU MMF and Goddard MMF), and preliminary results (the comparison with traditional GCMs), and (3) A discussion on the Goddard WRF version (its developments and applications).

A Coupled GCM-Cloud Resolving Modeling System, and A Regional Scale Model to Study Precipitation Processes

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo

2006-01-01

Recent GEWEX Cloud System Study (GCSS) model comparison projects have indicated that cloud-resolving models (CRMs) agree with observations better than traditional single-column models in simulating various types of clouds and cloud systems from different geographic locations. Current and future NASA satellite programs can provide cloud, precipitation, aerosol and other data at very fine spatial and temporal scales. It requires a coupled global circulation model (GCM) and cloud-scale model (termed a super-parameterization or multi-scale modeling framework, MMF) to use these satellite data to improve the understanding of the physical processes that are responsible for the variation in global and regional climate and hydrological systems. The use of a GCM will enable global coverage, and the use of a CRM will allow for better and more sophisticated physical parameterization. NASA satellite and field campaign cloud related datasets can provide initial conditions as well as validation for both the MMF and CRMs. The Goddard MMF is based on the 2D Goddard Cumulus Ensemble (GCE) model and the Goddard finite volume general circulation model (fvGCM), and it has started production runs with two years results (1998 and 1999). Also, at Goddard, we have implemented several Goddard microphysical schemes (21CE, several 31CE), Goddard radiation (including explicitly calculated cloud optical properties), and Goddard Land Information (LIS, that includes the CLM and NOAH land surface models) into a next generation regional scale model, WRF. In this talk, I will present: (1) A brief review on GCE model and its applications on precipitation processes (microphysical and land processes), (2) The Goddard MMF and the major difference between two existing MMFs (CSU MMF and Goddard MMF), and preliminary results (the comparison with traditional GCMs), and (3) A discussion on the Goddard WRF version (its developments and applications).

Predicting future US water yield and ecosystem productivity by linking an ecohydrological model to WRF dynamically downscaled climate projections

Treesearch

S. Sun; Ge Sun; Erika Cohen Mack; Steve McNulty; Peter Caldwell; K. Duan; Y. Zhang

2015-01-01

Quantifying the potential impacts of climate change on water yield and ecosystem productivity (i.e., carbon balances) is essential to developing sound watershed restoration plans, and climate change adaptation and mitigation strategies. This study links an ecohydrological model (Water Supply and Stress Index, WaSSI) with WRF (Weather Research and Forecasting Model)...

Achieving Superior Tropical Cyclone Intensity Forecasts by Improving the Assimilation of High-Resolution Satellite Data into Mesoscale Prediction Models

DTIC Science & Technology

2013-09-30

using polar orbit microwave and infrared sounder measurements from the Global Telecommunication System (GTS). The SDAT system was developed as a...WRF/GSI initial conditions and WRF boundary conditions. • WRF system to do short-range forecasts (6 hours) to provide the background fields for GSI...UCAR is related to a NASA GNSS proposal: “Improving Tropical Prediction and Analysis using COSMIC Radio Occultation Observations and an Ensemble Data

Evaluation of Diagnostic CO2 Flux and Transport Modeling in NU-WRF and GEOS-5

NASA Astrophysics Data System (ADS)

Kawa, S. R.; Collatz, G. J.; Tao, Z.; Wang, J. S.; Ott, L. E.; Liu, Y.; Andrews, A. E.; Sweeney, C.

2015-12-01

We report on recent diagnostic (constrained by observations) model simulations of atmospheric CO2 flux and transport using a newly developed facility in the NASA Unified-Weather Research and Forecast (NU-WRF) model. The results are compared to CO2 data (ground-based, airborne, and GOSAT) and to corresponding simulations from a global model that uses meteorology from the NASA GEOS-5 Modern Era Retrospective analysis for Research and Applications (MERRA). The objective of these intercomparisons is to assess the relative strengths and weaknesses of the respective models in pursuit of an overall carbon process improvement at both regional and global scales. Our guiding hypothesis is that the finer resolution and improved land surface representation in NU-WRF will lead to better comparisons with CO2 data than those using global MERRA, which will, in turn, inform process model development in global prognostic models. Initial intercomparison results, however, have generally been mixed: NU-WRF is better at some sites and times but not uniformly. We are examining the model transport processes in detail to diagnose differences in the CO2 behavior. These comparisons are done in the context of a long history of simulations from the Parameterized Chemistry and Transport Model, based on GEOS-5 meteorology and Carnegie Ames-Stanford Approach-Global Fire Emissions Database (CASA-GFED) fluxes, that capture much of the CO2 variation from synoptic to seasonal to global scales. We have run the NU-WRF model using unconstrained, internally generated meteorology within the North American domain, and with meteorological 'nudging' from Global Forecast System and North American Regional Reanalysis (NARR) in an effort to optimize the CO2 simulations. Output results constrained by NARR show the best comparisons to data. Discrepancies, of course, may arise either from flux or transport errors and compensating errors are possible. Resolving their interplay is also important to using the data in inverse models. Recent analysis is focused on planetary boundary depth, which can be significantly different between MERRA and NU-WRF, along with subgrid transport differences. Characterization of transport differences between the models will allow us to better constrain the CO2 fluxes, which is the major objective of this work.

Characterising Brazilian biomass burning emissions using WRF-Chem with MOSAIC sectional aerosol

NASA Astrophysics Data System (ADS)

Archer-Nicholls, S.; Lowe, D.; Darbyshire, E.; Morgan, W. T.; Bela, M. M.; Pereira, G.; Trembath, J.; Kaiser, J. W.; Longo, K. M.; Freitas, S. R.; Coe, H.; McFiggans, G.

2015-03-01

The South American Biomass Burning Analysis (SAMBBA) field campaign took detailed in situ flight measurements of aerosol during the 2012 dry season to characterise biomass burning aerosol and improve understanding of its impacts on weather and climate. Developments have been made to the Weather Research and Forecast model with chemistry (WRF-Chem) model to improve the representation of biomass burning aerosol in the region, by coupling a sectional aerosol scheme to the plume-rise parameterisation. Brazilian Biomass Burning Emissions Model (3BEM) fire emissions are used, prepared using PREP-CHEM-SRC, and mapped to CBM-Z and MOSAIC species. Model results have been evaluated against remote sensing products, AERONET sites, and four case studies of flight measurements from the SAMBBA campaign. WRF-Chem predicted layers of elevated aerosol loadings (5-20 μg sm-3) of particulate organic matter at high altitude (6-8 km) over tropical forest regions, while flight measurements showed a sharp decrease above 2-4 km altitude. This difference was attributed to the plume-rise parameterisation overestimating injection height. The 3BEM emissions product was modified using estimates of active fire size and burned area for the 2012 fire season, which reduced the fire size. The enhancement factor for fire emissions was increased from 1.3 to 5 to retain reasonable aerosol optical depths (AODs). The smaller fire size lowered the injection height of the emissions, but WRF-Chem still showed elevated aerosol loadings between 4-5 km altitude. Over eastern cerrado (savannah-like) regions, both modelled and measured aerosol loadings decreased above approximately 4 km altitude. Compared with MODIS satellite data and AERONET sites, WRF-Chem represented AOD magnitude well (between 0.3-1.5) over western tropical forest fire regions in the first half of the campaign, but tended to over-predict them in the second half, when precipitation was more significant. Over eastern cerrado regions, WRF-Chem tended to under-predict AODs. Modelled aerosol loadings in the east were higher in the modified emission scenario. The primary organic matter to black carbon ratio was typically between 8-10 in WRF-Chem. This was lower than the western flight measurements (interquartile range of 11.6-15.7 in B734, 14.7-24.0 in B739), but similar to the eastern flight B742 (8.1-10.4). However, single scattering albedo was close to measured over the western flights (0.87-0.89 in model; 0.86-0.91 in flight B734, and 0.81-0.95 in flight B739 measurements) but too high over the eastern flight B742 (0.86-0.87 in model, 0.79-0.82 in measurements). This suggests that improvements are needed to both modelled aerosol composition and optical properties calculations in WRF-Chem.

High-resolution regional climate simulations of precipitation and snowpack over the US northern Rockies in a changing climate

NASA Astrophysics Data System (ADS)

Wang, Y.; Geerts, B.; Liu, C.

2015-12-01

This work first examines the performance of a regional climate model in capturing orographic precipitation and snowpack dynamics in the northern US Rockies. The Weather Research and Forecasting (WRF) model is run at a sufficiently fine resolution (4-km horizontal grid spacing), over a sub-continental domain driven by the Climate Forecast System Reanalysis (CFSR), to examine WRF's ability to simulate the observed seasonal precipitation and snowpack dynamics. WRF retrospective simulations are being run over a 30-year period from 1980 to 2010. Observations from Snow Telemetry (SNOTEL, providing precipitation rate and snowpack snow water equivalent (SWE)) and the Parameter-elevation Regressions on Independent Slopes Model (PRISM, providing fine-scale monthly mean values of precipitation and temperature) are used for validation. The results show that WRF captures observed seasonal precipitation and snowpack build-up reasonably well. The second part of this work is in progress. A pseudo-global warming (PGW) technique is used to perturb the retrospective reanalysis with the anticipated change according to the consensus global model guidance under the CMIP5 "high emissions" (RCP8.5) scenario produced by the CCSM4. This technique preserves low-frequency general circulation patterns and the characteristics of storms entering the domain. The WRF model is rerun over 30 years centered on 2050 with perturbed initial and boundary conditions. The results will be used to examine the effect of climate variability and projected global warming on the statistical distributions of precipitation amounts and SWE in the studied domain.

Payette River Basin Project: Improving Operational Forecasting in Complex Terrain through Chemistry

NASA Astrophysics Data System (ADS)

Blestrud, D.; Kunkel, M. L.; Parkinson, S.; Holbrook, V. P.; Benner, S. G.; Fisher, J.

2015-12-01

Idaho Power Company (IPC) is an investor owned hydroelectric based utility, serving customers throughout southern Idaho and eastern Oregon. The University of Arizona (UA) runs an operational 1.8-km resolution Weather and Research Forecast (WRF) model for IPC, which is incorporated into IPC near and real-time forecasts for hydro, solar and wind generation, load servicing and a large-scale wintertime cloud seeding operation to increase winter snowpack. Winter snowpack is critical to IPC, as hydropower provides ~50% of the company's generation needs. In efforts to improve IPC's near-term forecasts and operational guidance to its cloud seeding program, IPC is working extensively with UA and the National Center for Atmospheric Research (NCAR) to improve WRF performance in the complex terrain of central Idaho. As part of this project, NCAR has developed a WRF based cloud seeding module (WRF CS) to deliver high-resolution, tailored forecasts to provide accurate guidance for IPC's operations. Working with Boise State University (BSU), IPC is conducting a multiyear campaign to validate the WRF CS's ability to account for and disperse the cloud seeding agent (AgI) within the boundary layer. This improved understanding of how WRF handles the AgI dispersion and fate will improve the understanding and ultimately the performance of WRF to forecast other parameters. As part of this campaign, IPC has developed an extensive ground based monitoring network including a Remote Area Snow Sampling Device (RASSD) that provides spatially and temporally discrete snow samples during active cloud seeding periods. To quantify AgI dispersion in the complex terrain, BSU conducts trace element analysis using LA-ICP-MS on the RASSD sampled snow to provide measurements (at the 10-12 level) of incorporated AgI, measurements are compare directly with WRF CS's estimates of distributed AgI. Modeling and analysis results from previous year's research and plans for coming seasons will be presented.

Application and Evaluation of MODIS LAI, FPAR, and Albedo ...

EPA Pesticide Factsheets

MODIS vegetation and albedo products provide a more realistic representation of surface conditions for input to the WRF/CMAQ modeling system. However, the initial evaluation of ingesting MODIS data into the system showed mixed results, with increased bias and error for 2-m temperature and reduced bias and error for 2-m mixing ratio. Recently, the WRF/CMAQ land surface and boundary laywer processes have been updated. In this study, MODIS vegetation and albedo data are input to the updated WRF/CMAQ meteorology and air quality simulations for 2006 over a North American (NA) 12-km domain. The evaluation of the simulation results shows that the updated WRF/CMAQ system improves 2-m temperature estimates over the pre-update base modeling system estimates. The MODIS vegetation input produces a realistic spring green-up that progresses through time from the south to north. Overall, MODIS input reduces 2-m mixing ration bias during the growing season. The NA west shows larger positive O3 bias during the growing season because of reduced gas phase deposition resulting from lower O3 deposition velocities driven by reduced vegetation cover. The O3 bias increase associated with the realistic vegetation representation indicates that further improvement may be needed in the WRF/CMAQ system. The National Exposure Research Laboratory’s Atmospheric Modeling Division (AMAD) conducts research in support of EPA’s mission to protect human health and the environment. AMAD’s rese

A Multi-Season Study of the Effects of MODIS Sea-Surface Temperatures on Operational WRF Forecasts at NWS Miami, FL

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; Santos, Pablo; Lazarus, Steven M.; Splitt, Michael E.; Haines, Stephanie L.; Dembek, Scott R.; Lapenta, William M.

2008-01-01

Studies at the Short-term Prediction Research and Transition (SPORT) Center have suggested that the use of Moderate Resolution Imaging Spectroradiometer (MODIS) sea-surface temperature (SST) composites in regional weather forecast models can have a significant positive impact on short-term numerical weather prediction in coastal regions. Recent work by LaCasse et al (2007, Monthly Weather Review) highlights lower atmospheric differences in regional numerical simulations over the Florida offshore waters using 2-km SST composites derived from the MODIS instrument aboard the polar-orbiting Aqua and Terra Earth Observing System satellites. To help quantify the value of this impact on NWS Weather Forecast Offices (WFOs), the SPORT Center and the NWS WFO at Miami, FL (MIA) are collaborating on a project to investigate the impact of using the high-resolution MODIS SST fields within the Weather Research and Forecasting (WRF) prediction system. The project's goal is to determine whether more accurate specification of the lower-boundary forcing within WRF will result in improved land/sea fluxes and hence, more accurate evolution of coastal mesoscale circulations and the associated sensible weather elements. The NWS MIA is currently running WRF in real-time to support daily forecast operations, using the National Centers for Environmental Prediction Nonhydrostatic Mesoscale Model dynamical core within the NWS Science and Training Resource Center's Environmental Modeling System (EMS) software. Twenty-seven hour forecasts are run dally initialized at 0300, 0900, 1500, and 2100 UTC on a domain with 4-km grid spacing covering the southern half of Florida and adjacent waters of the Gulf of Mexico and Atlantic Ocean. Each model run is initialized using the Local Analysis and Prediction System (LAPS) analyses available in AWIPS. The SSTs are initialized with the NCEP Real-Time Global (RTG) analyses at 1/12deg resolution (approx.9 km); however, the RTG product does not exhibit fine-scale details consistent with its grid resolution. SPORT is conducting parallel WRF EMS runs identical to the operational runs at NWS MIA except for the use of MODIS SST composites in place of the RTG product as the initial and boundary conditions over water, The MODIS SST composites for initializing the SPORT WRF runs are generated on a 2-km grid four times daily at 0400, 0700, 1600, and 1900 UTC, based on the times of the overhead passes of the Aqua and Terra satellites. The incorporation of the MODIS SST data into the SPORT WRF runs is staggered such that SSTs are updated with a new composite every six hours in each of the WRF runs. From mid-February to July 2007, over 500 parallel WRF simulations have been collected for analysis and verification. This paper will present verification results comparing the NWS MIA operational WRF runs to the SPORT experimental runs, and highlight any substantial differences noted in the predicted mesoscale phenomena for specific cases.

WRF Test on IBM BG/L:Toward High Performance Application to Regional Climate Research

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chin, H S

The effects of climate change will mostly be felt on local to regional scales (Solomon et al., 2007). To develop better forecast skill in regional climate change, an integrated multi-scale modeling capability (i.e., a pair of global and regional climate models) becomes crucially important in understanding and preparing for the impacts of climate change on the temporal and spatial scales that are critical to California's and nation's future environmental quality and economical prosperity. Accurate knowledge of detailed local impact on the water management system from climate change requires a resolution of 1km or so. To this end, a high performancemore » computing platform at the petascale appears to be an essential tool in providing such local scale information to formulate high quality adaptation strategies for local and regional climate change. As a key component of this modeling system at LLNL, the Weather Research and Forecast (WRF) model is implemented and tested on the IBM BG/L machine. The objective of this study is to examine the scaling feature of WRF on BG/L for the optimal performance, and to assess the numerical accuracy of WRF solution on BG/L.« less

Atmospheric transport simulations in support of the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE)

NASA Astrophysics Data System (ADS)

Henderson, J. M.; Eluszkiewicz, J.; Mountain, M. E.; Nehrkorn, T.; Chang, R. Y.-W.; Karion, A.; Miller, J. B.; Sweeney, C.; Steiner, N.; Wofsy, S. C.; Miller, C. E.

2014-10-01

This paper describes the atmospheric modeling that underlies the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) science analysis, including its meteorological and atmospheric transport components (Polar variant of the Weather Research and Forecasting (WRF) and Stochastic Time Inverted Lagrangian Transport (STILT) models), and provides WRF validation for May-October 2012 and March-November 2013 - the first two years of the aircraft field campaign. A triply nested computational domain for WRF was chosen so that the innermost domain with 3.3 km grid spacing encompasses the entire mainland of Alaska and enables the substantial orography of the state to be represented by the underlying high-resolution topographic input field. Summary statistics of the WRF model performance on the 3.3 km grid indicate good overall agreement with quality-controlled surface and radiosonde observations. Two-meter temperatures are generally too cold by approximately 1.4 K in 2012 and 1.1 K in 2013, while 2 m dewpoint temperatures are too low (dry) by 0.2 K in 2012 and too high (moist) by 0.6 K in 2013. Wind speeds are biased too low by 0.2 m s-1 in 2012 and 0.3 m s-1 in 2013. Model representation of upper level variables is very good. These measures are comparable to model performance metrics of similar model configurations found in the literature. The high quality of these fine-resolution WRF meteorological fields inspires confidence in their use to drive STILT for the purpose of computing surface influences ("footprints") at commensurably increased resolution. Indeed, footprints generated on a 0.1° grid show increased spatial detail compared with those on the more common 0.5° grid, lending itself better for convolution with flux models for carbon dioxide and methane across the heterogeneous Alaskan landscape. Ozone deposition rates computed using STILT footprints indicate good agreement with observations and exhibit realistic seasonal variability, further indicating that WRF-STILT footprints are of high quality and will support accurate estimates of CO2 and CH4 surface-atmosphere fluxes using CARVE observations.

Atmospheric transport simulations in support of the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE)

NASA Astrophysics Data System (ADS)

Henderson, J. M.; Eluszkiewicz, J.; Mountain, M. E.; Nehrkorn, T.; Chang, R. Y.-W.; Karion, A.; Miller, J. B.; Sweeney, C.; Steiner, N.; Wofsy, S. C.; Miller, C. E.

2015-04-01

This paper describes the atmospheric modeling that underlies the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) science analysis, including its meteorological and atmospheric transport components (polar variant of the Weather Research and Forecasting (WRF) and Stochastic Time Inverted Lagrangian Transport (STILT) models), and provides WRF validation for May-October 2012 and March-November 2013 - the first 2 years of the aircraft field campaign. A triply nested computational domain for WRF was chosen so that the innermost domain with 3.3 km grid spacing encompasses the entire mainland of Alaska and enables the substantial orography of the state to be represented by the underlying high-resolution topographic input field. Summary statistics of the WRF model performance on the 3.3 km grid indicate good overall agreement with quality-controlled surface and radiosonde observations. Two-meter temperatures are generally too cold by approximately 1.4 K in 2012 and 1.1 K in 2013, while 2 m dewpoint temperatures are too low (dry) by 0.2 K in 2012 and too high (moist) by 0.6 K in 2013. Wind speeds are biased too low by 0.2 m s-1 in 2012 and 0.3 m s-1 in 2013. Model representation of upper level variables is very good. These measures are comparable to model performance metrics of similar model configurations found in the literature. The high quality of these fine-resolution WRF meteorological fields inspires confidence in their use to drive STILT for the purpose of computing surface influences ("footprints") at commensurably increased resolution. Indeed, footprints generated on a 0.1° grid show increased spatial detail compared with those on the more common 0.5° grid, better allowing for convolution with flux models for carbon dioxide and methane across the heterogeneous Alaskan landscape. Ozone deposition rates computed using STILT footprints indicate good agreement with observations and exhibit realistic seasonal variability, further indicating that WRF-STILT footprints are of high quality and will support accurate estimates of CO2 and CH4 surface-atmosphere fluxes using CARVE observations.

Air Quality Modeling for the Urban Jackson, Mississippi Region Using a High Resolution WRF/Chem Model

PubMed Central

Yerramilli, Anjaneyulu; Dodla, Venkata B.; Desamsetti, Srinivas; Challa, Srinivas V.; Young, John H.; Patrick, Chuck; Baham, Julius M.; Hughes, Robert L.; Yerramilli, Sudha; Tuluri, Francis; Hardy, Mark G.; Swanier, Shelton J.

2011-01-01

In this study, an attempt was made to simulate the air quality with reference to ozone over the Jackson (Mississippi) region using an online WRF/Chem (Weather Research and Forecasting–Chemistry) model. The WRF/Chem model has the advantages of the integration of the meteorological and chemistry modules with the same computational grid and same physical parameterizations and includes the feedback between the atmospheric chemistry and physical processes. The model was designed to have three nested domains with the inner-most domain covering the study region with a resolution of 1 km. The model was integrated for 48 hours continuously starting from 0000 UTC of 6 June 2006 and the evolution of surface ozone and other precursor pollutants were analyzed. The model simulated atmospheric flow fields and distributions of NO2 and O3 were evaluated for each of the three different time periods. The GIS based spatial distribution maps for ozone, its precursors NO, NO2, CO and HONO and the back trajectories indicate that all the mobile sources in Jackson, Ridgeland and Madison contributing significantly for their formation. The present study demonstrates the applicability of WRF/Chem model to generate quantitative information at high spatial and temporal resolution for the development of decision support systems for air quality regulatory agencies and health administrators. PMID:21776240

Methods for Improving Fine-Scale Applications of the WRF-CMAQ Modeling System

EPA Science Inventory

Presentation on the work in AMAD to improve fine-scale (e.g. 4km and 1km) WRF-CMAQ simulations. Includes iterative analysis, updated sea surface temperature and snow cover fields, and inclusion of impervious surface information (urban parameterization).

ESPC Coupled Global Prediction System

DTIC Science & Technology

2015-09-30

numerical transport algorithms. Adapted from WRF , a Semi-Lagrangian advection scheme is being implemented in the vertical in NAVGEM to process the...used in the sedimentation of cloud species, especially in the WRF research-community model for all cloud microphysics modules. We have started to

UPDATE ON DEVELOPMENT OF NUDGING FDDA FOR ADVANCED RESEARCH WRF

EPA Science Inventory

A nudging-based four-dimensional data assimilation (FDDA) system is being developed for the Weather Research and Forecasting (WRF) Model. This effort represents a collaboration between The Pennsylvania State University (i.e., Penn State), the National Center for Atmospheric Rese...

Extending flood forecasting lead time in large basin by coupling bias-corrected WRF QPF with distributed hydrological model

NASA Astrophysics Data System (ADS)

LI, J.; Chen, Y.; Wang, H. Y.

2016-12-01

In large basin flood forecasting, the forecasting lead time is very important. Advances in numerical weather forecasting in the past decades provides new input to extend flood forecasting lead time in large rivers. Challenges for fulfilling this goal currently is that the uncertainty of QPF with these kinds of NWP models are still high, so controlling the uncertainty of QPF is an emerging technique requirement.The Weather Research and Forecasting (WRF) model is one of these NWPs, and how to control the QPF uncertainty of WRF is the research topic of many researchers among the meteorological community. In this study, the QPF products in the Liujiang river basin, a big river with a drainage area of 56,000 km2, was compared with the ground observation precipitation from a rain gauge networks firstly, and the results show that the uncertainty of the WRF QPF is relatively high. So a post-processed algorithm by correlating the QPF with the observed precipitation is proposed to remove the systematical bias in QPF. With this algorithm, the post-processed WRF QPF is close to the ground observed precipitation in area-averaged precipitation. Then the precipitation is coupled with the Liuxihe model, a physically based distributed hydrological model that is widely used in small watershed flash flood forecasting. The Liuxihe Model has the advantage with gridded precipitation from NWP and could optimize model parameters when there are some observed hydrological data even there is only a few, it also has very high model resolution to improve model performance, and runs on high performance supercomputer with parallel algorithm if executed in large rivers. Two flood events in the Liujiang River were collected, one was used to optimize the model parameters and another is used to validate the model. The results show that the river flow simulation has been improved largely, and could be used for real-time flood forecasting trail in extending flood forecasting leading time.

Effects of different regional climate model resolution and forcing scales on projected hydrologic changes

NASA Astrophysics Data System (ADS)

Mendoza, Pablo A.; Mizukami, Naoki; Ikeda, Kyoko; Clark, Martyn P.; Gutmann, Ethan D.; Arnold, Jeffrey R.; Brekke, Levi D.; Rajagopalan, Balaji

2016-10-01

We examine the effects of regional climate model (RCM) horizontal resolution and forcing scaling (i.e., spatial aggregation of meteorological datasets) on the portrayal of climate change impacts. Specifically, we assess how the above decisions affect: (i) historical simulation of signature measures of hydrologic behavior, and (ii) projected changes in terms of annual water balance and hydrologic signature measures. To this end, we conduct our study in three catchments located in the headwaters of the Colorado River basin. Meteorological forcings for current and a future climate projection are obtained at three spatial resolutions (4-, 12- and 36-km) from dynamical downscaling with the Weather Research and Forecasting (WRF) regional climate model, and hydrologic changes are computed using four different hydrologic model structures. These projected changes are compared to those obtained from running hydrologic simulations with current and future 4-km WRF climate outputs re-scaled to 12- and 36-km. The results show that the horizontal resolution of WRF simulations heavily affects basin-averaged precipitation amounts, propagating into large differences in simulated signature measures across model structures. The implications of re-scaled forcing datasets on historical performance were primarily observed on simulated runoff seasonality. We also found that the effects of WRF grid resolution on projected changes in mean annual runoff and evapotranspiration may be larger than the effects of hydrologic model choice, which surpasses the effects from re-scaled forcings. Scaling effects on projected variations in hydrologic signature measures were found to be generally smaller than those coming from WRF resolution; however, forcing aggregation in many cases reversed the direction of projected changes in hydrologic behavior.

Impacts of Different Anthropogenic Aerosol Emission Scenarios on Hydrology in the Mekong Basins and their Effects on Irrigation and Hydropower

NASA Astrophysics Data System (ADS)

Yeo, L. K.; Wang, C.

2016-12-01

Water distribution is closely linked to food and energy security. Aerosol emissions affect cloud properties, as well as atmospheric stability, changing the distribution of precipitation. These changes in precipitation causes changes in water availability, affecting food production and energy generation. These impacts are especially important in Southeast Asia, which uses up to 90% of their water supply for irrigation. In addition, the Mekong river, the largest inland fishery in the world, has 30,000MW of hydropower potential in its lower reaches alone. Modelling the impacts of these anthropogenic emission scenarios will allow us to better understand their downstream effects on hydrology, and any potential feedbacks it may have on future aerosol emissions. In the first step, we run the WRF model using FNL reanlaysis data from 2014 and 2015 to generate the WRF-hydro model forcing inputs. We then run the WRF-hydro model and compare the output with current measurements of soil moisture, river flow, and precipitation. Secondly, we run the WRF-Chem model with various anthropogenic emission scenarios and put the results through the WRF-hydro model to determine the impact of these emission scenarios on soil moisture and river flow. The scenarios include enhanced anthropogenic emissions in Asia, anologous to widespread adoption of coal burning as an energy source in Asia. Anthropogenic emissions have the potential to affect energy policy in countries affected by these emissions. When hydropower generation is affected by changes in precipitation, the affected countries will have to switch to alternative sources of fuel to meet their energy needs. These sources typically result in changes in anthropogenic aerosol emisssions, especially if coal is used as an alternative source of energy.

Coupling Spectral-bin Cloud Microphysics with the MOSAIC Aerosol Model in WRF-Chem: Methodology and Results for Marine Stratocumulus Clouds

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gao, Wenhua; Fan, Jiwen; Easter, Richard C.

Aerosol-cloud interaction processes can be represented more physically with bin cloud microphysics relative to bulk microphysical parameterizations. However, due to computational power limitations in the past, bin cloud microphysics was often run with very simple aerosol treatments. The purpose of this study is to represent better aerosol-cloud interaction processes in the Chemistry version of Weather Research and Forecast model (WRF-Chem) at convection-permitting scales by coupling spectral-bin cloud microphysics (SBM) with the MOSAIC sectional aerosol model. A flexible interface is built that exchanges cloud and aerosol information between them. The interface contains a new bin aerosol activation approach, which replaces themore » treatments in the original SBM. It also includes the modified aerosol resuspension and in-cloud wet removal processes with the droplet loss tendencies and precipitation fluxes from SBM. The newly coupled system is evaluated for two marine stratocumulus cases over the Southeast Pacific Ocean with either a simplified aerosol setup or full-chemistry. We compare the aerosol activation process in the newly-coupled SBM-MOSAIC against the SBM simulation without chemistry using a simplified aerosol setup, and the results show consistent activation rates. A longer time simulation reinforces that aerosol resuspension through cloud drop evaporation plays an important role in replenishing aerosols and impacts cloud and precipitation in marine stratocumulus clouds. Evaluation of the coupled SBM-MOSAIC with full-chemistry using aircraft measurements suggests that the new model works realistically for the marine stratocumulus clouds, and improves the simulation of cloud microphysical properties compared to a simulation using MOSAIC coupled with the Morrison two-moment microphysics.« less

Intercomparison of planetary boundary layer parameterization and its impacts on surface ozone concentration in the WRF/Chem model for a case study in Houston/Texas

NASA Astrophysics Data System (ADS)

Cuchiara, G. C.; Li, X.; Carvalho, J.; Rappenglück, B.

2014-10-01

With over 6 million inhabitants the Houston metropolitan area is the fourth-largest in the United States. Ozone concentration in this southeast Texas region frequently exceeds the National Ambient Air Quality Standard (NAAQS). For this reason our study employed the Weather Research and Forecasting model with Chemistry (WRF/Chem) to quantify meteorological prediction differences produced by four widely used PBL schemes and analyzed its impact on ozone predictions. The model results were compared to observational data in order to identify one superior PBL scheme better suited for the area. The four PBL schemes include two first-order closure schemes, the Yonsei University (YSU) and the Asymmetric Convective Model version 2 (ACM2); as well as two turbulent kinetic energy closure schemes, the Mellor-Yamada-Janjic (MYJ) and Quasi-Normal Scale Elimination (QNSE). Four 24 h forecasts were performed, one for each PBL scheme. Simulated vertical profiles for temperature, potential temperature, relative humidity, water vapor mixing ratio, and the u-v components of the wind were compared to measurements collected during the Second Texas Air Quality Study (TexAQS-II) Radical and Aerosol Measurements Project (TRAMP) experiment in summer 2006. Simulated ozone was compared against TRAMP data, and air quality stations from Continuous Monitoring Station (CAMS). Also, the evolutions of the PBL height and vertical mixing properties within the PBL for the four simulations were explored. Although the results yielded high correlation coefficients and small biases in almost all meteorological variables, the overall results did not indicate any preferred PBL scheme for the Houston case. However, for ozone prediction the YSU scheme showed greatest agreements with observed values.

Intercomparison of Planetary Boundary Layer Parameterization and its Impacts on Surface Ozone Concentration in the WRF/Chem Model for a Case Study in Houston/Texas

NASA Astrophysics Data System (ADS)

Cuchiara, Gustavo C.; Li, Xiangshang; Carvalho, Jonas; Rappenglück, Bernhard

2015-04-01

With over 6 million inhabitants the Houston metropolitan area is the fourth-largest in the United States. Ozone concentration in this southeast Texas region frequently exceeds the National Ambient Air Quality Standard (NAAQS). For this reason our study employed the Weather Research and Forecasting model with Chemistry (WRF/Chem) to quantify meteorological prediction differences produced by four widely used PBL schemes and analyzed its impact on ozone predictions. The model results were compared to observational data in order to identify one superior PBL scheme better suited for the area. The four PBL schemes include two first-order closure schemes, the Yonsei University (YSU) and the Asymmetric Convective Model version 2 (ACM2); as well as two turbulent kinetic energy closure schemes, the Mellor-Yamada-Janjic (MYJ) and Quasi-Normal Scale Elimination (QNSE). Four 24 h forecasts were performed, one for each PBL scheme. Simulated vertical profiles for temperature, potential temperature, relative humidity, water vapor mixing ratio, and the u-v components of the wind were compared to measurements collected during the Second Texas Air Quality Study (TexAQS-II) Radical and Aerosol Measurements Project (TRAMP) experiment in summer 2006. Simulated ozone was compared against TRAMP data, and air quality stations from Continuous Monitoring Station (CAMS). Also, the evolutions of the PBL height and vertical mixing properties within the PBL for the four simulations were explored. Although the results yielded high correlation coefficients and small biases in almost all meteorological variables, the overall results did not indicate any preferred PBL scheme for the Houston case. However, for ozone prediction the YSU scheme showed greatest agreements with observed values.

Validation of WRF-Chem air quality simulations in the Netherlands at high resolution

NASA Astrophysics Data System (ADS)

Hilboll, A.; Lowe, D.; Kuenen, J. J. P.; Denier Van Der Gon, H.; Vrekoussis, M.

2017-12-01

Air pollution is the single most important environmental hazard for publichealth, and especially nitrogen dioxide (NO2) plays a key role in air qualityresearch. With the aim of improving the quality and reproducibility ofmeasurements of NO2 vertical distribution from MAX-DOAS instruments, theCINDI-2 campaign was held in Cabauw (NL) in September 2016.The measurement site was rural, but surrounded by several major pollutioncenters. Due to this spatial heterogeneity of emissions, as well as themeteorological conditions, high spatial and temporal variability in NO2 mixingratios were observed.Air quality models used in the analysis of the measured data must have highspatial resolution in order to resolve this fine spatial structure. Thisremains a challenge even today, mostly due to the uncertainties and largespatial heterogeneity of emission data, and the need to parameterize small-scaleprocesses.In this study, we use the state-of-the-art version 3.9 of the Weather Researchand Forecasting Model with Chemistry (WRF-Chem) to simulate air pollutantconcentrations over the Netherlands, to facilitate the analysis of the CINDI-2NO2 measurements. The model setup contains three nested domains withhorizontal resolutions of 15, 3, and 1 km. Anthropogenic emissions are takenfrom the TNO-MACC III inventory and, where available, from the Dutch PollutantRelease and Transfer Register (Emissieregistratie), at a spatial resolution of 7and 1 km, respectively. We use the Common Reactive Intermediates gas-phasechemical mechanism (CRIv2-R5) with the MOSAIC aerosol module.The high spatial resolution of model and emissions will allow us to resolve thestrong spatial gradients in the NO2 concentrations measured during theCINDI-2 campaign, allowing for an unprecedented level of detail in theanalysis of individual pollution sources.

Mesoscale Modeling of Smoke Particles Distribution and Their Radiative Feedback over Northern Sub-Saharan African Region

NASA Astrophysics Data System (ADS)

Yue, Y.; Wang, J.; Ichoku, C. M.; Ellison, L.

2015-12-01

Stretching from southern boundary of Sahara to the equator and expanding west to east from Atlantic Ocean coasts to the India Ocean coasts, the northern sub-Saharan African (NSSA) region has been subject to intense biomass burning. Comprised of savanna, shrub, tropical forest and a number of agricultural crops, the extensive fires burn belt covers central and south of NSSA during dry season (from October to March) contributes to one of the highest biomass burning rate per km2 in the world. Due to smoke particles' absorption effects of solar radiation, they can modify the surface and atmosphere temperature and thus change atmospheric stability, height of the boundary layer, regional atmospheric circulation, evaporation rate, cloud formation, and precipitation. Hence, smoke particles emitted from biomass burning over NSSA region has a significant influence to the air quality, weather and climate variability. In this study, the first version of this Fire Energetics and Emissions Research (FEER.v1) emissions of several smoke constituents including light-absorbing organic carbon (OC) and black carbon (BC) are applied to a state-of-science meteorology-chemistry model as NOAA Weather Research and Forecasting Model with Chemistry (WRF-Chem). We analyzed WRF-Chem simulations of surface and vertical distribution of various pollutants and their direct radiative effects in conjunction with satellite observation data from Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar data with Orthogonal Polarization (CALIPSO) to strengthen the importance of combining space measured emission products like FEER.v1 emission inventory with mesoscale model over intense biomass burning region, especially in area where ground-based air-quality and radiation-related observations are limited or absent.

Coupling spectral-bin cloud microphysics with the MOSAIC aerosol model in WRF-Chem: Methodology and results for marine stratocumulus clouds

NASA Astrophysics Data System (ADS)

Gao, Wenhua; Fan, Jiwen; Easter, R. C.; Yang, Qing; Zhao, Chun; Ghan, Steven J.

2016-09-01

Aerosol-cloud interaction processes can be represented more physically with bin cloud microphysics relative to bulk microphysical parameterizations. However, due to computational power limitations in the past, bin cloud microphysics was often run with very simple aerosol treatments. The purpose of this study is to represent better aerosol-cloud interaction processes in the Chemistry version of Weather Research and Forecast model (WRF-Chem) at convection-permitting scales by coupling spectral-bin cloud microphysics (SBM) with the MOSAIC sectional aerosol model. A flexible interface is built that exchanges cloud and aerosol information between them. The interface contains a new bin aerosol activation approach, which replaces the treatments in the original SBM. It also includes the modified aerosol resuspension and in-cloud wet removal processes with the droplet loss tendencies and precipitation fluxes from SBM. The newly coupled system is evaluated for two marine stratocumulus cases over the Southeast Pacific Ocean with either a simplified aerosol setup or full-chemistry. We compare the aerosol activation process in the newly coupled SBM-MOSAIC against the SBM simulation without chemistry using a simplified aerosol setup, and the results show consistent activation rates. A longer time simulation reinforces that aerosol resuspension through cloud drop evaporation plays an important role in replenishing aerosols and impacts cloud and precipitation in marine stratocumulus clouds. Evaluation of the coupled SBM-MOSAIC with full-chemistry using aircraft measurements suggests that the new model works realistically for the marine stratocumulus clouds, and improves the simulation of cloud microphysical properties compared to a simulation using MOSAIC coupled with the Morrison two-moment microphysics.

Assessment of Land Surface Models in a High-Resolution Atmospheric Model during Indian Summer Monsoon

NASA Astrophysics Data System (ADS)

Attada, Raju; Kumar, Prashant; Dasari, Hari Prasad

2018-04-01

Assessment of the land surface models (LSMs) on monsoon studies over the Indian summer monsoon (ISM) region is essential. In this study, we evaluate the skill of LSMs at 10 km spatial resolution in simulating the 2010 monsoon season. The thermal diffusion scheme (TDS), rapid update cycle (RUC), and Noah and Noah with multi-parameterization (Noah-MP) LSMs are chosen based on nature of complexity, that is, from simple slab model to multi-parameterization options coupled with the Weather Research and Forecasting (WRF) model. Model results are compared with the available in situ observations and reanalysis fields. The sensitivity of monsoon elements, surface characteristics, and vertical structures to different LSMs is discussed. Our results reveal that the monsoon features are reproduced by WRF model with all LSMs, but with some regional discrepancies. The model simulations with selected LSMs are able to reproduce the broad rainfall patterns, orography-induced rainfall over the Himalayan region, and dry zone over the southern tip of India. The unrealistic precipitation pattern over the equatorial western Indian Ocean is simulated by WRF-LSM-based experiments. The spatial and temporal distributions of top 2-m soil characteristics (soil temperature and soil moisture) are well represented in RUC and Noah-MP LSM-based experiments during the ISM. Results show that the WRF simulations with RUC, Noah, and Noah-MP LSM-based experiments significantly improved the skill of 2-m temperature and moisture compared to TDS (chosen as a base) LSM-based experiments. Furthermore, the simulations with Noah, RUC, and Noah-MP LSMs exhibit minimum error in thermodynamics fields. In case of surface wind speed, TDS LSM performed better compared to other LSM experiments. A significant improvement is noticeable in simulating rainfall by WRF model with Noah, RUC, and Noah-MP LSMs over TDS LSM. Thus, this study emphasis the importance of choosing/improving LSMs for simulating the ISM phenomena in a regional model.

Numerical simulations of an advection fog event over Shanghai Pudong International Airport with the WRF model

NASA Astrophysics Data System (ADS)

Lin, Caiyan; Zhang, Zhongfeng; Pu, Zhaoxia; Wang, Fengyun

2017-10-01

A series of numerical simulations is conducted to understand the formation, evolution, and dissipation of an advection fog event over Shanghai Pudong International Airport (ZSPD) with the Weather Research and Forecasting (WRF) model. Using the current operational settings at the Meteorological Center of East China Air Traffic Management Bureau, the WRF model successfully predicts the fog event at ZSPD. Additional numerical experiments are performed to examine the physical processes associated with the fog event. The results indicate that prediction of this particular fog event is sensitive to microphysical schemes for the time of fog dissipation but not for the time of fog onset. The simulated timing of the arrival and dissipation of the fog, as well as the cloud distribution, is substantially sensitive to the planetary boundary layer and radiation (both longwave and shortwave) processes. Moreover, varying forecast lead times also produces different simulation results for the fog event regarding its onset and duration, suggesting a trade-off between more accurate initial conditions and a proper forecast lead time that allows model physical processes to spin up adequately during the fog simulation. The overall outcomes from this study imply that the complexity of physical processes and their interactions within the WRF model during fog evolution and dissipation is a key area of future research.

Investigating the Effects of Grid Resolution of WRF Model for Simulating the Atmosphere for use in the Study of Wake Turbulence

NASA Astrophysics Data System (ADS)

Prince, Alyssa; Trout, Joseph; di Mercurio, Alexis

2017-01-01

The Weather Research and Forecasting (WRF) Model is a nested-grid, mesoscale numerical weather prediction system maintained by the Developmental Testbed Center. The model simulates the atmosphere by integrating partial differential equations, which use the conservation of horizontal momentum, conservation of thermal energy, and conservation of mass along with the ideal gas law. This research investigated the possible use of WRF in investigating the effects of weather on wing tip wake turbulence. This poster shows the results of an investigation into the accuracy of WRF using different grid resolutions. Several atmospheric conditions were modeled using different grid resolutions. In general, the higher the grid resolution, the better the simulation, but the longer the model run time. This research was supported by Dr. Manuel A. Rios, Ph.D. (FAA) and the grant ``A Pilot Project to Investigate Wake Vortex Patterns and Weather Patterns at the Atlantic City Airport by the Richard Stockton College of NJ and the FAA'' (13-G-006). Dr. Manuel A. Rios, Ph.D. (FAA), and the grant ``A Pilot Project to Investigate Wake Vortex Patterns and Weather Patterns at the Atlantic City Airport by the Richard Stockton College of NJ and the FAA''

Applications of Satellite Remote Sensing Products to Enhance and Evaluate the AIRPACT Regional Air Quality Modeling System

NASA Astrophysics Data System (ADS)

Herron-Thorpe, F. L.; Mount, G. H.; Emmons, L. K.; Lamb, B. K.; Jaffe, D. A.; Wigder, N. L.; Chung, S. H.; Zhang, R.; Woelfle, M.; Vaughan, J. K.; Leung, F. T.

2013-12-01

The WSU AIRPACT air quality modeling system for the Pacific Northwest forecasts hourly levels of aerosols and atmospheric trace gases for use in determining potential health and ecosystem impacts by air quality managers. AIRPACT uses the WRF/SMOKE/CMAQ modeling framework, derives dynamic boundary conditions from MOZART-4 forecast simulations with assimilated MOPITT CO, and uses the BlueSky framework to derive fire emissions. A suite of surface measurements and satellite-based remote sensing data products across the AIRPACT domain are used to evaluate and improve model performance. Specific investigations include anthropogenic emissions, wildfire simulations, and the effects of long-range transport on surface ozone. In this work we synthesize results for multiple comparisons of AIRPACT with satellite products such as IASI ammonia, AIRS carbon monoxide, MODIS AOD, OMI tropospheric ozone and nitrogen dioxide, and MISR plume height. Features and benefits of the newest version of AIRPACT's web-interface are also presented.

Mapping Nuclear Fallout Using the Weather Research & Forecasting (WRF) Model

DTIC Science & Technology

2012-09-01

relevant modules, originally designed to predict the settling of volcanic ash, such that a stabilized cloud of nuclear particulate is initialized...within the model. This modified code is then executed for various atmospheric test explosions and the results are qualitatively and quantitatively...HYSPLIT Simulation ....................................... 44  Figure 7. WRF Fallout Prediction for Test Shot George, 0.8 R/h at H+1

Modeling of air pollutant removal by dry deposition to urban trees using a WRF/CMAQ/i-Tree Eco coupled system

Treesearch

Maria Theresa I. Cabaraban; Charles N. Kroll; Satoshi Hirabayashi; David J. Nowak

2013-01-01

A distributed adaptation of i-Tree Eco was used to simulate dry deposition in an urban area. This investigation focused on the effects of varying temperature, LAI, and NO2 concentration inputs on estimated NO2 dry deposition to trees in Baltimore, MD. A coupled modeling system is described, wherein WRF provided temperature...

Simulating the impacts of chronic ozone exposure on plant conductance and photosynthesis, and on the regional hydroclimate using WRF/Chem

NASA Astrophysics Data System (ADS)

Li, Jialun; Mahalov, Alex; Hyde, Peter

2016-11-01

The Noah-Multiparameterization land surface model in the Weather Research and Forecasting (WRF) with Chemistry (WRF/Chem) is modified to include the effects of chronic ozone exposure (COE) on plant conductance and photosynthesis (PCP) found from field experiments. Based on the modified WRF/Chem, the effects of COE on regional hydroclimate have been investigated over the continental United States. Our results indicate that the model with/without modification in its current configuration can reproduce the rainfall and temperature patterns of the observations and reanalysis data, although it underestimates rainfall in the central Great Plains and overestimates it in the eastern coast states. The experimental tests on the effects of COE include setting different thresholds of ambient ozone concentrations ([O3]) and using different linear regressions to quantify PCP against the COE. Compared with the WRF/Chem control run (i.e., without considering the effects of COE), the modified model at different experiment setups improves the simulated estimates of rainfall and temperatures in Texas and regions to the immediate north. The simulations in June, July and August of 2007-2012 show that surface [O3] decrease latent heat fluxes (LH) by 10-27 W m-2, increase surface air temperatures (T 2) by 0.6 °C-2.0 °C, decrease rainfall by 0.9-1.4 mm d-1, and decrease runoff by 0.1-0.17 mm d-1 in Texas and surrounding areas, all of which highly depends on the precise experiment setup, especially the [O3] threshold. The mechanism producing these results is that COE decreases the LH and increases sensible heat fluxes, which in turn increases the Bowen ratios and air temperatures. This lowering of the LH also results in the decrease of convective potential and finally decreases convective rainfall. Employing this modified WRF/Chem model in any high [O3] region can improve the understanding of the interactions of vegetation, meteorology, chemistry/emissions, and crop productivity.

Design and Impacts of Land-Biogenic-Atmosphere Coupling in the NASA-Unified WRF (NU-WRF) Modeling System

NASA Technical Reports Server (NTRS)

Tan, Qian; Santanello, Joseph A., Jr.; Zhou, Shujia; Tao, Zhining; Peters-Lidard, Christa d.; Chn, Mian

2011-01-01

Land-Atmosphere coupling is typically designed and implemented independently for physical (e.g. water and energy) and chemical (e.g. biogenic emissions and surface depositions)-based models and applications. Differences in scale, data requirements, and physics thus limit the ability of Earth System models to be fully coupled in a consistent manner. In order for the physical-chemical-biological coupling to be complete, treatment of the land in terms of surface classification, condition, fluxes, and emissions must be considered simultaneously and coherently across all components. In this study, we investigate a coupling strategy for the NASA-Unified Weather Research and Forecasting (NU-WRF) model that incorporates the traditionally disparate fluxes of water and energy through NASA's LIS (Land Information System) and biogenic emissions through BEIS (Biogenic Emissions Inventory System) and MEGAN (Model of Emissions of Gases and Aerosols from Nature) into the atmosphere. In doing so, inconsistencies across model inputs and parameter data are resolved such that the emissions from a particular plant species are consistent with the heat and moisture fluxes calculated for that land cover type. In turn, the response of the atmospheric turbulence and mixing in the planetary boundary layer (PBL) acts on the identical surface type, fluxes, and emissions for each. In addition, the coupling of dust emission within the NU-WRF system is performed in order to ensure consistency and to maximize the benefit of high-resolution land representation in LIS. The impacts of those self-consistent components on' the simulation of atmospheric aerosols are then evaluated through the WRF-Chem-GOCART (Goddard Chemistry Aerosol Radiation and Transport) model. Overall, this ambitious project highlights the current difficulties and future potential of fully coupled. components. in Earth System models, and underscores the importance of the iLEAPS community in supporting improved knowledge of processes and innovative approaches for models and observations.

Satellite Sounder Data Assimilation for Improving Alaska Region Weather Forecast

NASA Technical Reports Server (NTRS)

Zhu, Jiang; Stevens, E.; Zavodsky, B. T.; Zhang, X.; Heinrichs, T.; Broderson, D.

2014-01-01

Data assimilation has been demonstrated very useful in improving both global and regional numerical weather prediction. Alaska has very coarser surface observation sites. On the other hand, it gets much more satellite overpass than lower 48 states. How to utilize satellite data to improve numerical prediction is one of hot topics among weather forecast community in Alaska. The Geographic Information Network of Alaska (GINA) at University of Alaska is conducting study on satellite data assimilation for WRF model. AIRS/CRIS sounder profile data are used to assimilate the initial condition for the customized regional WRF model (GINA-WRF model). Normalized standard deviation, RMSE, and correlation statistic analysis methods are applied to analyze one case of 48 hours forecasts and one month of 24-hour forecasts in order to evaluate the improvement of regional numerical model from Data assimilation. The final goal of the research is to provide improved real-time short-time forecast for Alaska regions.

Advanced Land Surface Processes in the Coupled WRF/CMAQ with MODIS Input

EPA Science Inventory

Land surface modeling (LSM) is important in WRF/CMAQ for simulating the exchange of heat, moisture, momentum, trace atmospheric chemicals, and windblown dust between the land surface and the atmosphere.? Vegetation and soil treatments are crucial in LSM for surface energy budgets...

Incorporating GOES Satellite Photosynthetically Active Radiation (PAR) Retrievals to Improve Biogenic Emission Estimates in Texas

NASA Astrophysics Data System (ADS)

Zhang, Rui; White, Andrew T.; Pour Biazar, Arastoo; McNider, Richard T.; Cohan, Daniel S.

2018-01-01

This study examines the influence of insolation and cloud retrieval products from the Geostationary Operational Environmental Satellite (GOES) system on biogenic emission estimates and ozone simulations in Texas. Compared to surface pyranometer observations, satellite-retrieved insolation and photosynthetically active radiation (PAR) values tend to systematically correct the overestimation of downwelling shortwave radiation in the Weather Research and Forecasting (WRF) model. The correlation coefficient increases from 0.93 to 0.97, and the normalized mean error decreases from 36% to 21%. The isoprene and monoterpene emissions estimated by the Model of Emissions of Gases and Aerosols from Nature are on average 20% and 5% less, respectively, when PAR from the direct satellite retrieval is used rather than the control WRF run. The reduction in biogenic emission rates using satellite PAR reduced the predicted maximum daily 8 h ozone concentration by up to 5.3 ppbV over the Dallas-Fort Worth (DFW) region on some days. However, episode average ozone response is less sensitive, with a 0.6 ppbV decrease near DFW and 0.3 ppbV increase over East Texas. The systematic overestimation of isoprene concentrations in a WRF control case is partially corrected by using satellite PAR, which observes more clouds than are simulated by WRF. Further, assimilation of GOES-derived cloud fields in WRF improved CAMx model performance for ground-level ozone over Texas. Additionally, it was found that using satellite PAR improved the model's ability to replicate the spatial pattern of satellite-derived formaldehyde columns and aircraft-observed vertical profiles of isoprene.

Effect of MERRA-2 initial and boundary conditions on WRF-Chem aerosol simulations over the Arabian Peninsula

NASA Astrophysics Data System (ADS)

Ukhov, Alexander; Stenchikov, Georgiy

2017-04-01

In this study, we test the sensitivity of the horizontal and vertical distributions of aerosols to the initial and boundary conditions (IC&BC) of the aerosol/chemistry. We use the WRF-Chem model configured over the Arabian Peninsula to study both dust and anthropogenic aerosols. Currently, in the WRF-Chem the aerosol/chemistry IC&BC are constructed using either default aerosol/chemistry profiles with no inflow of aerosols and chemicals through the lateral boundaries or using the aerosol/chemistry fields from MOZART, the model for ozone and related chemical tracers from the NCAR. Here, we construct aerosol/chemistry IC&BC using MERRA-2 output. MERRA-2 is a recently developed reanalysis that assimilates ground-based and satellite observations to provide the improved distributions of aerosols and chemical species. We ran WRF-Chem simulations for July-August 2015 using GOCART/AFWA dust emission and GOCART aerosol schemes. We used the EDGAR HTAP V4 dataset to calculate SO2 emissions. Comparison of three runs initiated using the same ERA-Interim reanalysis fields but different aerosol/chemistry IC&BC (default WRF-Chem, MOZART, and MERRA-2) with AERONET, Micropulse Lidar, Balloon, and satellite observations shows that the MERRA-2 IC&BC are superior.

Resolving vorticity-driven lateral fire spread using the WRF-Fire coupled atmosphere-fire numerical model

NASA Astrophysics Data System (ADS)

Simpson, C. C.; Sharples, J. J.; Evans, J. P.

2014-05-01

Fire channelling is a form of dynamic fire behaviour, during which a wildland fire spreads rapidly across a steep lee-facing slope in a direction transverse to the background winds, and is often accompanied by a downwind extension of the active flaming region and extreme pyro-convection. Recent work using the WRF-Fire coupled atmosphere-fire model has demonstrated that fire channelling can be characterised as vorticity-driven lateral fire spread (VDLS). In this study, 16 simulations are conducted using WRF-Fire to examine the sensitivity of resolving VDLS to spatial resolution and atmosphere-fire coupling within the WRF-Fire model framework. The horizontal grid spacing is varied between 25 and 90 m, and the two-way atmosphere-fire coupling is either enabled or disabled. At high spatial resolution, the atmosphere-fire coupling increases the peak uphill and lateral spread rate by a factor of up to 2.7 and 9.5. The enhancement of the uphill and lateral spread rate diminishes at coarser spatial resolution, and VDLS is not modelled for a horizontal grid spacing of 90 m. The laterally spreading fire fronts become the dominant contributors of the extreme pyro-convection. The resolved fire-induced vortices responsible for driving the lateral spread in the coupled simulations have non-zero vorticity along each unit vector direction, and develop due to an interaction between the background winds and vertical return circulations generated at the flank of the fire front as part of the pyro-convective updraft. The results presented in this study demonstrate that both high spatial resolution and two-way atmosphere-fire coupling are required to reproduce VDLS within the current WRF-Fire model framework.

Forecasting Lightning Threat Using WRF Proxy Fields

NASA Technical Reports Server (NTRS)

McCaul, E. W., Jr.

2010-01-01

Objectives: Given that high-resolution WRF forecasts can capture the character of convective outbreaks, we seek to: 1. Create WRF forecasts of LTG threat (1-24 h), based on 2 proxy fields from explicitly simulated convection: - graupel flux near -15 C (captures LTG time variability) - vertically integrated ice (captures LTG threat area). 2. Calibrate each threat to yield accurate quantitative peak flash rate densities. 3. Also evaluate threats for areal coverage, time variability. 4. Blend threats to optimize results. 5. Examine sensitivity to model mesh, microphysics. Methods: 1. Use high-resolution 2-km WRF simulations to prognose convection for a diverse series of selected case studies. 2. Evaluate graupel fluxes; vertically integrated ice (VII). 3. Calibrate WRF LTG proxies using peak total LTG flash rate densities from NALMA; relationships look linear, with regression line passing through origin. 4. Truncate low threat values to make threat areal coverage match NALMA flash extent density obs. 5. Blend proxies to achieve optimal performance 6. Study CAPS 4-km ensembles to evaluate sensitivities.

Downscaling with a nested regional climate model in near-surface fields over the contiguous United States: WRF dynamical downscaling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Jiali; Kotamarthi, Veerabhadra R.

The Weather Research and Forecasting (WRF) model is used for dynamic downscaling of 2.5 degree National Centers for Environmental Prediction-U.S. Department of Energy Reanalysis II (NCEP-R2) data for 1980-2010 at 12 km resolution over most of North America. The model's performance for surface air temperature and precipitation is evaluated by comparison with high-resolution observational data sets. The model's ability to add value is investigated by comparison with NCEP-R2 data and a 50 km regional climate simulation. The causes for major model bias are studied through additional sensitivity experiments with various model setup/integration approaches and physics representations. The WRF captures themore » main features of the spatial patterns and annual cycles of air temperature and precipitation over most of the contiguous United States. However, simulated air temperatures over the south central region and precipitation over the Great Plains and the Southwest have significant biases. Allowing longer spin-up time, reducing the nudging strength, or replacing the WRF Single-Moment 6-class microphysics with Morrison microphysics reduces the bias over some subregions. However, replacing the Grell-Devenyi cumulus parameterization with Kain-Fritsch shows no improvement. The 12 km simulation does add value above the NCEP-R2 data and the 50 km simulation over mountainous and coastal zones.« less

Meteorological air quality forecasting using the WRF-Chem model during the LMOS2017 field campaign

NASA Astrophysics Data System (ADS)

Stanier, C. O.; Abdioskouei, M.; Carmichael, G. R.; Christiansen, M.; Sobhani, N.

2017-12-01

The Lake Michigan Ozone Study (LMOS 2017) occurred during May and June 2017 to address the high ozone episodes in coastal communities surrounding Lake Michigan. Aircraft, ship, mobile lab, and ground-based stations were used in this campaign to build an extensive dataset regarding ozone, its precursors, and particulate matter. The University of Iowa produced high-resolution (4x4 km2 horizontal resolution and 53 vertical levels) forecast products using the WRF-Chem modeling system in support of experimental planning during LMOS 2017. The base forecast system used WRF-Chem 3.6.1 and updated National Emission Inventory (NEI-2011v2). In the updated NEI-2011v2, we reduced the NOx emissions by 28% based on EPA's estimated NOx trends from 2011 to 2017. We ran another daily forecast (perturbed forecast) with 50% reduced NOx emission to capture the sensitivity of ozone to NOx emission and account for the impact of weekend emissions on ozone values. Preliminary in-field evaluation of model performance for clouds, on-shore flows, and surface and aircraft sampled ozone and NOx concentrations found that the model successfully captured much of the observed synoptic variability of onshore flows. The model captured the variability of O3 well, but underpredicted peak ozone during high O3 episodes. In post-campaign WRF-Chem simulations, we investigated the sensitivity of the model to the hydrocarbon emission.

Prognostic significance of dilated inferior vena cava in advanced decompensated heart failure.

PubMed

Lee, Hsin-Fu; Hsu, Lung-An; Chang, Chi-Jen; Chan, Yi-Hsin; Wang, Chun-Li; Ho, Wan-Jing; Chu, Pao-Hsien

2014-10-01

Dilated inferior vena cava (IVC) is prevalent among patients with heart failure (HF), but whether its presence predicts worsening renal function (WRF) or adverse outcomes is unclear. This cohort study analyzed patients with left ventricular ejection fraction <40 % and repeated hospitalizations (≥2 times) for HF between August 2009 and August 2011. The study endpoints were death and HF re-hospitalization. Among baseline parameters, IVC diameter was the most powerful predictor for the development of WRF (area under the curve = 0.795, cut-off value = 20.5 mm). During the 2-year follow-up, 36 patients (49 %) were re-hospitalized for HF and 14 patients (19 %) died. The event rates were significantly greater in the WRF group than in the non-WRF group (71 vs. 30 %, P < 0.001 for HF re-hospitalization; 29 vs. 10 %, P = 0.03 for death). In Cox regression model, the risk of combined end-points was increased in patients with aging, elevated blood urine nitrogen, IVC >21 mm, and WRF. When adjusted for confounding factors, IVC >21 mm [hazard ratio (HR) 3.73, 95 % confidence interval (CI) 1.66-8.34] and WRF (HR 2.68, 95 % CI 1.07-6.75) were significant predictors for adverse outcomes. In patients with advanced decompensated HF, dilated IVC (>21 mm) predicted the development of WRF and could be a predictor for adverse outcomes.

Evaluation of the Consistency among In Situ and Remote Sensing Measurements of CO2 over North America using the CarbonTracker-Lagrange Regional Inverse Modeling Framework

NASA Astrophysics Data System (ADS)

Andrews, A. E.; Trudeau, M.; Hu, L.; Thoning, K. W.; Shiga, Y. P.; Michalak, A. M.; Benmergui, J. S.; Mountain, M. E.; Nehrkorn, T.; O'Dell, C.; Jacobson, A. R.; Miller, J.; Sweeney, C.; Chen, H.; Ploeger, F.; Tans, P. P.

2017-12-01

CarbonTracker-Lagrange (CT-L) is a regional inverse modeling system for estimating CO2 fluxes with rigorous uncertainty quantification. CT-L uses footprints from the Stochastic Time-Inverted Lagrangian Transport (STILT) model driven by high-resolution (10 to 30 km) meteorological fields from the Weather Research and Forecasting (WRF) model. We have computed a library of footprints corresponding to in situ and remote sensing measurements of CO2 over North America for 2007-2015. GOSAT and OCO-2 XCO2 retrievals are simulated using a suite of CT-L terrestrial ecosystem flux estimates that have been optimized with respect to in situ atmospheric CO2 measurements along with fossil fuel fluxes from emissions inventories. A vertical profile of STILT-WRF footprints was constructed corresponding to each simulated satellite retrieval, and CO2 profiles are generated by convolving the footprints with fluxes and attaching initial values advected from the domain boundaries. The stratospheric contribution to XCO2 has been estimated using 4-dimensional CO2 fields from the NOAA CarbonTracker model (version CT2016) and from the Chemical Lagrangian Model of the Stratosphere (CLaMS), after scaling the model fields to match data from the NOAA AirCore surface-to-stratosphere air sampling system. Tropospheric lateral boundary conditions are from CT2016 and from an empirical boundary value product derived from aircraft and marine boundary layer data. The averaging kernel and a priori CO2 profile are taken into account for direct comparisons with retrievals. We have focused on North America due to the relatively dense in situ measurements available with the aim of developing strategies for combined assimilation of in situ and remote sensing data. We will consider the extent to which interannual variability in terrestrial fluxes is manifest in the real and simulated satellite retrievals, and we will investigate possible systematic biases in the satellite retrievals and in the model.

ManUniCast: A Community Weather and Air-Quality Forecasting Teaching Portal

NASA Astrophysics Data System (ADS)

Schultz, David M.; Anderson, Stuart; Fairman, Jonathan G.; Lowe, Douglas; McFiggans, Gordon; Lee, Elsa; Seo-Zindy, Ryo

2014-05-01

Manunicast was borne out of the needs of our teaching program: students were entering a world where environmental prediction via numerical model was an essential skill, but were not exposed to the production or output of such models. Our site is an educational testbed to explain to students and the public how weather, air-quality, and air-chemistry forecasts are made using real-time predictions as examples. As far as we know, this site provides the first freely available real-time predictions for the UK. We perform two simulations a day over three domains using the most popular, freely available, community atmospheric mesoscale and chemistry models WRF-ARW and WRF-Chem: 1. a WRF-ARW domain over the North Atlantic and western Europe (20-km horizontal grid spacing) 2. a WRF-ARW domain over the UK and Ireland (4-km grid spacing, nested within the 20-km domain) 3. a WRF-Chem domain over the UK and Ireland (12-km grid spacing) Called ManUniCast (Manchester University Forecast), we offer a suite of products from horizontal maps, time series at stations (meteograms), skew-T-logp charts, and cross sections to help students better visualize the weather and the relationships between the various fields more effectively, specifically through the ability to overlay and fade between different plotted products. This presentation discusses how we funded and built ManUniCast, the struggles we faced, and its use in our classes.

“Fine-Scale Application of the coupled WRF-CMAQ System to ...

EPA Pesticide Factsheets

The DISCOVER-AQ project (Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality), is a joint collaboration between NASA, U.S. EPA and a number of other local organizations with the goal of characterizing air quality in urban areas using satellite, aircraft, vertical profiler and ground based measurements (http://discover-aq.larc.nasa.gov). In July 2011, the DISCOVER-AQ project conducted intensive air quality measurements in the Baltimore, MD and Washington, D.C. area in the eastern U.S. To take advantage of these unique data, the Community Multiscale Air Quality (CMAQ) model, coupled with the Weather Research and Forecasting (WRF) model is used to simulate the meteorology and air quality in the same region using 12-km, 4-km and 1-km horizontal grid spacings. The goal of the modeling exercise is to demonstrate the capability of the coupled WRF-CMAQ modeling system to simulate air quality at fine grid spacings in an urban area. Development of new data assimilation techniques and the use of higher resolution input data for the WRF model have been implemented to improve the meteorological results, particularly at the 4-km and 1-km grid resolutions. In addition, a number of updates to the CMAQ model were made to enhance the capability of the modeling system to accurately represent the magnitude and spatial distribution of pollutants at fine model resolutions. Data collected during the 2011 DISCOVER-AQ campa

“Application and evaluation of the two-way coupled WRF ...

EPA Pesticide Factsheets

The DISCOVER-AQ project (Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality), is a joint collaboration between NASA, U.S. EPA and a number of other local organizations with the goal of characterizing air quality in urban areas using satellite, aircraft, vertical profiler and ground based measurements (http://discover-aq.larc.nasa.gov). In July 2011, the DISCOVER-AQ project conducted intensive air quality measurements in the Baltimore, MD and Washington, D.C. area in the eastern U.S. To take advantage of these unique data, the Community Multiscale Air Quality (CMAQ) model, coupled with the Weather Research and Forecasting (WRF) model is used to simulate the meteorology and air quality in the same region using 12-km, 4-km and 1-km horizontal grid spacings. The goal of the modeling exercise is to demonstrate the capability of the coupled WRF-CMAQ modeling system to simulate air quality at fine grid spacings in an urban area. Development of new data assimilation techniques and the use of higher resolution input data for the WRF model have been implemented to improve the meteorological results, particularly at the 4-km and 1-km grid resolutions. In addition, a number of updates to the CMAQ model were made to enhance the capability of the modeling system to accurately represent the magnitude and spatial distribution of pollutants at fine model resolutions. Data collected during the 2011 DISCOVER-AQ campa

Application and Evaluation of MODIS LAI, FPAR, and Albedo Products in the WRF/CMAQ System

EPA Science Inventory

MODIS vegetation and albedo products provide a more realistic representation of surface conditions for input to the WRF/CMAQ modeling system. However, the initial evaluation of ingesting MODIS data into the system showed mixed results, with increased bias and error for 2-m temper...

Dynamic evaluation of two decades of WRF-CMAQ ozone simulations over the contiguous United States

EPA Science Inventory

Dynamic evaluation of the fully coupled Weather Research and Forecasting (WRF)– Community Multi-scale Air Quality (CMAQ) model ozone simulations over the contiguous United States (CONUS) using two decades of simulations covering the period from 1990 to 2010 is conducted to ...

Dynamic evaluation of two decades of WRF-CMAQ ozone simulations over the contiguous United States

EPA Science Inventory

Dynamic evaluation of the fully coupled Weather Research and Forecasting (WRF)– Community Multi-scale Air Quality (CMAQ) model ozone simulations over the contiguous United States (CONUS) using two decades of simulations covering the period from 1990 to 2010 is conducted to assess...

Application and evaluation of the WRF-CMAQ modeling system to the 2011 DISCOVER-AQ Baltimore-Washington D.C. study

NASA Astrophysics Data System (ADS)

Appel, W.; Gilliam, R. C.; Pouliot, G. A.; Godowitch, J. M.; Pleim, J.; Hogrefe, C.; Kang, D.; Roselle, S. J.; Mathur, R.

2013-12-01

The DISCOVER-AQ project (Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality), is a joint collaboration between NASA, U.S. EPA and a number of other local organizations with the goal of characterizing air quality in urban areas using satellite, aircraft, vertical profiler and ground based measurements (http://discover-aq.larc.nasa.gov). In July 2011, the DISCOVER-AQ project conducted intensive air quality measurements in the Baltimore, MD and Washington, D.C. area in the eastern U.S. To take advantage of these unique data, the Community Multiscale Air Quality (CMAQ) model, coupled with the Weather Research and Forecasting (WRF) model is used to simulate the meteorology and air quality in the same region using 12-km, 4-km and 1-km horizontal grid spacings. The goal of the modeling exercise is to demonstrate the capability of the coupled WRF-CMAQ modeling system to simulate air quality at fine grid spacings in an urban area. Development of new data assimilation techniques and the use of higher resolution input data for the WRF model have been implemented to improve the meteorological results, particularly at the 4-km and 1-km grid resolutions. In addition, a number of updates to the CMAQ model were made to enhance the capability of the modeling system to accurately represent the magnitude and spatial distribution of pollutants at fine model resolutions. Data collected during the 2011 DISCOVER-AQ campaign, which include aircraft transects and spirals, ship measurements in the Chesapeake Bay, ozonesondes, tethered balloon measurements, DRAGON aerosol optical depth measurements, LIDAR measurements, and intensive ground-based site measurements, are used to evaluate results from the WRF-CMAQ modeling system for July 2011 at the three model grid resolutions. The results of the comparisons of the model results to these measurements will be presented, along with results from the various sensitivity simulations examining the impact the various updates to the modeling system have on the model estimates.

Use of High-resolution WRF Simulations to Forecast Lightning Threat

NASA Technical Reports Server (NTRS)

McCaul, William E.; LaCasse, K.; Goodman, S. J.

2006-01-01

Recent observational studies have confirmed the existence of a robust statistical relationship between lightning flash rates and the amount of large precipitating ice hydrometeors in storms. This relationship is exploited, in conjunction with the capabilities of recent forecast models such as WRF, to forecast the threat of lightning from convective storms using the output fields from the model forecasts. The simulated vertical flux of graupel at -15C is used in this study as a proxy for charge separation processes and their associated lightning risk. Six-h simulations are conducted for a number of case studies for which three-dimensional lightning validation data from the North Alabama Lightning Mapping Array are available. Experiments indicate that initialization of the WRF model on a 2 km grid using Eta boundary conditions, Doppler radar radial velocity and reflectivity fields, and METAR and ACARS data yield the most realistic simulations. An array of subjective and objective statistical metrics are employed to document the utility of the WRF forecasts. The simulation results are also compared to other more traditional means of forecasting convective storms, such as those based on inspection of the convective available potential energy field.

Impact of bias-corrected reanalysis-derived lateral boundary conditions on WRF simulations

NASA Astrophysics Data System (ADS)

Moalafhi, Ditiro Benson; Sharma, Ashish; Evans, Jason Peter; Mehrotra, Rajeshwar; Rocheta, Eytan

2017-08-01

Lateral and lower boundary conditions derived from a suitable global reanalysis data set form the basis for deriving a dynamically consistent finer resolution downscaled product for climate and hydrological assessment studies. A problem with this, however, is that systematic biases have been noted to be present in the global reanalysis data sets that form these boundaries, biases which can be carried into the downscaled simulations thereby reducing their accuracy or efficacy. In this work, three Weather Research and Forecasting (WRF) model downscaling experiments are undertaken to investigate the impact of bias correcting European Centre for Medium range Weather Forecasting Reanalysis ERA-Interim (ERA-I) atmospheric temperature and relative humidity using Atmospheric Infrared Sounder (AIRS) satellite data. The downscaling is performed over a domain centered over southern Africa between the years 2003 and 2012. The sample mean and the mean as well as standard deviation at each grid cell for each variable are used for bias correction. The resultant WRF simulations of near-surface temperature and precipitation are evaluated seasonally and annually against global gridded observational data sets and compared with ERA-I reanalysis driving field. The study reveals inconsistencies between the impact of the bias correction prior to downscaling and the resultant model simulations after downscaling. Mean and standard deviation bias-corrected WRF simulations are, however, found to be marginally better than mean only bias-corrected WRF simulations and raw ERA-I reanalysis-driven WRF simulations. Performances, however, differ when assessing different attributes in the downscaled field. This raises questions about the efficacy of the correction procedures adopted.

Oceanic response to tropical cyclone `Phailin' in the Bay of Bengal

NASA Astrophysics Data System (ADS)

Pant, V.; Prakash, K. R.

2016-02-01

Vertical mixing largely explains surface cooling induced by Tropical Cyclones (TCs). However, TC-induced upwelling of deeper waters plays an important role as it partly balances the warming of subsurface waters induced by vertical mixing. Below 100 m, vertical advection results in cooling that persists for a few days after the storm. The present study investigates the integrated ocean response to tropical cyclone `Phaillin' (10-14 October 2013) in the Bay of Bengal (BoB) through both coupled and stand-alone ocean-atmosphere models. Two numerical experiments with different coupling configurations between Regional Ocean Modelling System (ROMS) and Weather Research and Forecasting (WRF) were performed to investigate the impact of Phailin cyclone on the surface and sub-surface oceanic parameters. In the first experiment, ocean circulation model ROMS observe surface wind forcing from a mesoscale atmospheric model (WRF with nested damin setup), while rest forcing parameters are supplied to ROMS from NCEP data. In the second experiment, all surface forcing data to ROMS directly comes from WRF. The modeling components and data fields exchanged between atmospheric and oceanic models are described. The coupled modeling system is used to identify model sensitivity by exchanging prognostic variable fields between the two model components during simulation of Phallin cyclone (10-14 October 2013) in the BoB.In general, the simulated Phailin cyclone track and intensities agree well with observations in WRF simulations. Further, the inter-comparison between stand-alone and coupled model simulations validated against observations highlights better performance of coupled modeling system in simulating the oceanic conditions during the Phailin cyclone event.

Ensemble Simulation of Sierra Nevada Snowmelt Runoff Using a Regional Climate Modeling Approach

NASA Astrophysics Data System (ADS)

Holtzman, N.; Pavelsky, T.; Wrzesien, M.

2017-12-01

The snowmelt-dominated watersheds on the western slopes of the California Sierra Nevada drain into reservoirs that generate electricity and help irrigate Central Valley farms. At the end of the wet season of each year, around April 1, most of the water that will become runoff in these basins is stored as snow at high elevations. Snow measurements provide a good estimate of the total annual runoff to come. For efficient water management, however, it is also useful to know the timing of runoff. When and how large will the peak flow into a reservoir be, and how fast will the flow decline after it peaks? We address such questions using a coupled regional climate and land surface model, WRF and Noah-MP, to dynamically downscale the North American Regional Reanalysis (NARR) with an ensemble approach. First, we assess several methods of deriving melt-season runoff from WRF. We run WRF for a complete water year, and also test initializing WRF snow from observation-based datasets at the approximate date of peak snow water equivalent. By aggregating the modeled runoffs over the drainage basins of reservoirs and comparing to naturalized flow data, we can assess the basin-scale snow accumulation accuracy of WRF and the other datasets in the Sierra. After choosing a procedure to set the model snow at the end of the wet season, we apply in WRF the melt-season meteorology from 20 different past years of NARR to produce an ensemble of simulations, each with modeled flows into 8 reservoirs spanning the Sierra. We use the ensemble to characterize the likely spread in the timing and magnitude of hydrologic outcomes during the melt season. Probabilistic forecasts can help water-energy systems operate more efficiently. The ensemble also shows the effect of warm-season temperature extremes on flow timing, allowing human systems to prepare for those possibilities. Finally, the ensemble provides a baseline estimate of the maximum variability in runoff timing that could be generated by past conditions. If future runoff patterns consistently exceed the extremes found in the ensemble, nonstationary hydroclimate can be inferred.

Potential aetiologies and prognostic implications of worsening renal function in acute decompensated heart failure.

PubMed

Abo-Salem, Elsayed; Sherif, Khalid; Dunlap, Stephanie; Prabhakar, Sharma

2014-12-01

One third of patients hospitalized for acute decompensated heart failure (ADHF) develop a worsening renal function (WRF) that is associated with increased in-hospital morbidity and mortality. However, previous investigations have not evaluated the various etiologies of WRF and its impact on prognosis. A retrospective chart review was performed of patients admitted with ADHF who had a rise of serum creatinine ≥ 0.3 mg/dl on admission or during their hospital stay. The chart notes were reviewed for the suggested etiology of WRF. Cases were defined as ADHF associated WRF (ADHF-WRF) when there was no other explanation for WRF, plus an objective evidence of hypervolemia. Cases with WRF after 48 hours of a negative fluid balance were classified as diuresis-associated WRF (DA-WRF). ICD-9 codes identified 319 admissions with ADHF complicated with WRF. Fifty admissions were excluded. The most common causes of WRF were ADHF-WRF (43.1%) and DA-WRF (42.8%). Other causes included nephrotoxins (5.9%) and surgery (3.7%). The mortality rate was significantly lower with DA-WRF compared to ADHF-WRF; odds ratio 0.059 (95% CI 0.007 to 0.45, P = 0.006). Readmission at 30 days was higher in cases with ADHF-WRF (42%). WRF with ADHF is a heterogeneous group, and cases with ADHF-WRF had a higher in-hospital mortality and readmission rates.

Extreme precipitation forecasting in the Chilean Andean region with complex topography using the Weather Research and Forecasting (WRF) model

NASA Astrophysics Data System (ADS)

Gironás, J.; Yáñez Morroni, G.; Caneo, M.; Delgado, R.

2017-12-01

The Weather Research and Forecasting (WRF) model is broadly used for weather forecasting, hindcasting and researching due to its good performance. However, the atmospheric conditions for simulating are not always optimal when it includes complex topographies: affecting WRF mathematical stability and convergence, therefore, its performance. As Chile is a country strongly characterized by a complex topography and high gradients of elevation, WRF is ineffective resolving Chilean mountainous terrain and foothills. The need to own an effective weather forecasting tool relies on that Chile's main cities are located in these regions. Furthermore, the most intense rainfall events take place here, commonly caused by the presence of cutoff lows. This work analyzes a microphysics scheme ensemble to enhance initial forecasts made by the Chilean Weather Agency (DMC). These forecasts were made over the Santiago piedmont, in Quebrada de Ramón watershed, located upstream an urban area highly populated. In this region a non-existing planning increases the potential damage of a flash flood. An initial testing was made over different vertical levels resolution (39 and 50 levels), and subsequently testing with land use and surface models, and finally with the initial and boundary condition data (GFS/FNL). Our task made emphasis in analyzing microphysics and lead time (3 to 5 days before the storm peak) in the computational simulations over three extreme rainfall events between 2015 and 2017. WRF shortcoming are also related to the complex configuration of the synoptic events, even when the steep topography difficult the rainfall event peak amount, and to a lesser degree, the exact rainfall event beginning prediction. No evident trend was found in the lead time, but as expected, better results in rainfall and zero isotherm height are obtained with smaller anticipation. We found that WRF do predict properly the N-hours with the biggest amount of rainfall (5 hours corresponding to Quebrada de Ramón's time of concentration) and the temperatures during the event. This is a fundamental input to a hydrological model that could forecast flash floods. Finally, WSM-6Class microphysics was chosen as the one with best performance, but a geostatistical approach to countervail WRF forecasts' shortcomings over Andean piedmont is required.

Investigating Anomalies in the Output Generated by the Weather Research and Forecasting (WRF) Model

NASA Astrophysics Data System (ADS)

Decicco, Nicholas; Trout, Joseph; Manson, J. Russell; Rios, Manny; King, David

2015-04-01

The Weather Research and Forecasting (WRF) model is an advanced mesoscale numerical weather prediction (NWP) model comprised of two numerical cores, the Numerical Mesoscale Modeling (NMM) core, and the Advanced Research WRF (ARW) core. An investigation was done to determine the source of erroneous output generated by the NMM core. In particular were the appearance of zero values at regularly spaced grid cells in output fields and the NMM core's evident (mis)use of static geographic information at a resolution lower than the nesting level for which the core is performing computation. A brief discussion of the high-level modular architecture of the model is presented as well as methods utilized to identify the cause of these problems. Presented here are the initial results from a research grant, ``A Pilot Project to Investigate Wake Vortex Patterns and Weather Patterns at the Atlantic City Airport by the Richard Stockton College of NJ and the FAA''.

Estimating the importance of factors influencing the radon-222 flux from building walls.

PubMed

Girault, Frédéric; Perrier, Frédéric

2012-09-01

Radiation hazard in dwellings is dominated by the contribution of radon-222 released from soil and bedrock, but the contribution of building materials can also be important. Using a simple air mixing model in a 2-story house with an attic and a basement, it is estimated that a significant risk arises when the Wall Radon exhalation Flux (WRF) exceeds 10×10(-3) Bq·m(-2)·s(-1). WRF is studied using a multiphase advection-diffusion 3-layer analytical model with advective flow, possibly induced by a pressure deficit inside the house compared with the outside atmosphere. To first order, in most circumstances, the WRF is proportional to the wall thickness and to the radon source term, the effective radium concentration EC(Ra), which is the product of the radium-226 concentration by the emanation coefficient E. The WRF decreases with increasing material porosity and exhibits a maximum for water saturation of about 50%. For EC(Ra)=10 Bq·kg(-1), in many instances, WRF is larger than 10×10(-3) Bq·m(-2)·s(-1) and, therefore, EC(Ra)=10 Bq·kg(-1) can be considered as the typical limit not to be exceeded by building materials. An upper limit of the WRF is obtained in the purely advective regime, independent of porosity or moisture content, which can thus be used as a robust safety guideline. The sensitivity of WRF to temperature, due to the temperature sensitivity of EC(Ra) or the temperature sensitivity of radon Henry constant can be larger than 5% for the seasonal variation in the presence of slight pressure deficit. The temperature sensitivity of EC(Ra) is the dominant effect, except for moist walls. Temperature and moisture variation effects on the WRF potentially can account for most observed seasonal variations of radon concentration in houses, in addition to seasonal changes of air exchange, suggesting that the contribution of walls should be considered when designing remediation strategies and studied with dedicated experiments. Copyright © 2012 Elsevier B.V. All rights reserved.

The Role of Surface Energy Exchange for Simulating Wind Inflow: An Evaluation of Multiple Land Surface Models in WRF for the Southern Great Plains Site Field Campaign Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wharton, Sonia; Simpson, Matthew; Osuna, Jessica

The Weather Research and Forecasting (WRF) model is used to investigate choice of land surface model (LSM) on the near-surface wind profile, including heights reached by multi-megawatt wind turbines. Simulations of wind profiles and surface energy fluxes were made using five LSMs of varying degrees of sophistication in dealing with soil-plant-atmosphere feedbacks for the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility’s Southern Great Plains (SGP) Central Facility in Oklahoma. Surface-flux and wind-profile measurements were available for validation. The WRF model was run for three two-week periods during which varying canopy and meteorological conditions existed. Themore » LSMs predicted a wide range of energy-flux and wind-shear magnitudes even during the cool autumn period when we expected less variability. Simulations of energy fluxes varied in accuracy by model sophistication, whereby LSMs with very simple or no soil-plant-atmosphere feedbacks were the least accurate; however, the most complex models did not consistently produce more accurate results. Errors in wind shear also were sensitive to LSM choice and were partially related to the accuracy of energy flux data. The variability of LSM performance was relatively high, suggesting that LSM representation of energy fluxes in the WRF model remains a significant source of uncertainty for simulating wind turbine inflow conditions.« less

The Impact of Microphysics on Intensity and Structure of Hurricanes

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Shi, Jainn; Lang, Steve; Peters-Lidard, Christa

2006-01-01

During the past decade, both research and operational numerical weather prediction models, e.g. Weather Research and Forecast (WRF) model, have started using more complex microphysical schemes originally developed for high-resolution cloud resolving models (CRMs) with a 1-2 km or less horizontal resolutions. WFW is a next-generation mesoscale forecast model and assimilation system that has incorporated modern software framework, advanced dynamics, numeric and data assimilation techniques, a multiple moveable nesting capability, and improved physical packages. WFW model can be used for a wide range of applications, from idealized research to operational forecasting, with an emphasis on horizontal grid sizes in the range of 1-10 km. The current WRF includes several different microphysics options such as Lin et al. (1983), WSM 6-class and Thompson microphysics schemes. We have recently implemented three sophisticated cloud microphysics schemes into WRF. The cloud microphysics schemes have been extensively tested and applied for different mesoscale systems in different geographical locations. The performances of these schemes have been compared to those from other WRF microphysics options. We are performing sensitivity tests in using WW to examine the impact of six different cloud microphysical schemes on hurricane track, intensity and rainfall forecast. We are also performing the inline tracer calculation to comprehend the physical processes @e., boundary layer and each quadrant in the boundary layer) related to the development and structure of hurricanes.

Study of Regional Volcanic Impact on the Middle East and North Africa using high-resolution global and regional models

NASA Astrophysics Data System (ADS)

Osipov, Sergey; Dogar, Mohammad; Stenchikov, Georgiy

2016-04-01

High-latitude winter warming after strong equatorial volcanic eruptions caused by circulation changes associated with the anomalously positive phase of Arctic Oscillation is a subject of active research during recent decade. But severe winter cooling in the Middle East observed after the Mt. Pinatubo eruption of 1991, although recognized, was not thoroughly investigated. These severe regional climate perturbations in the Middle East cannot be explained by solely radiative volcanic cooling, which suggests that a contribution of forced circulation changes could be important and significant. To better understand the mechanisms of the Middle East climate response and evaluate the contributions of dynamic and radiative effects we conducted a comparative study using Geophysical Fluid Dynamics Laboratory global High Resolution Atmospheric Model (HiRAM) with the effectively "regional-model-resolution" of 25-km and the regional Weather Research and Forecasting (WRF) model focusing on the eruption of Mount Pinatubo on June 15, 1991 followed by a pronounced positive phase of the Arctic Oscillation. The WRF model has been configured over the Middle East and North Africa (MENA) region. The WRF code has been modified to interactively account for the radiative effect of volcanic aerosols. Both HiRAM and WRF capture the main features of the MENA climate response and show that in winter the dynamic effects in the Middle East prevail the direct radiative cooling from volcanic aerosols.

Comparing Aircraft Observations of Snowfall to Forecasts Using Single or Two Moment Bulk Water Microphysics Schemes

NASA Technical Reports Server (NTRS)

Molthan, Andrew L.

2010-01-01

High resolution weather forecast models with explicit prediction of hydrometeor type, size distribution, and fall speed may be useful in the development of precipitation retrievals, by providing representative characteristics of frozen hydrometeors. Several single or double-moment microphysics schemes are currently available within the Weather Research and Forecasting (WRF) model, allowing for the prediction of up to three ice species. Each scheme incorporates different assumptions regarding the characteristics of their ice classes, particularly in terms of size distribution, density, and fall speed. In addition to the prediction of hydrometeor content, these schemes must accurately represent the vertical profile of water vapor to account for possible attenuation, along with the size distribution, density, and shape characteristics of ice crystals that are relevant to microwave scattering. An evaluation of a particular scheme requires the availability of field campaign measurements. The Canadian CloudSat/CALIPSO Validation Project (C3VP) obtained measurements of ice crystal shapes, size distributions, fall speeds, and precipitation during several intensive observation periods. In this study, C3VP observations obtained during the 22 January 2007 synoptic-scale snowfall event are compared against WRF model output, based upon forecasts using four single-moment and two double-moment schemes available as of version 3.1. Schemes are compared against aircraft observations by examining differences in size distribution, density, and content. In addition to direct measurements from aircraft probes, simulated precipitation can also be converted to equivalent, remotely sensed characteristics through the use of the NASA Goddard Satellite Data Simulator Unit. Outputs from high resolution forecasts are compared against radar and satellite observations emphasizing differences in assumed crystal shape and size distribution characteristics.

Climate Change, Public Health, and Decision Support: The New Threat of Vector-borne Disease

NASA Astrophysics Data System (ADS)

Grant, F.; Kumar, S.

2011-12-01

Climate change and vector-borne diseases constitute a massive threat to human development. It will not be enough to cut emissions of greenhouse gases-the tide of the future has already been established. Climate change and vector-borne diseases are already undermining the world's efforts to reduce extreme poverty. It is in the best interests of the world leaders to think in terms of concerted global actions, but adaptation and mitigation must be accomplished within the context of local community conditions, resources, and needs. Failure to act will continue to consign developed countries to completely avoidable health risks and significant expense. Failure to act will also reduce poorest of the world's population-some 2.6 billion people-to a future of diminished opportunity. Northrop Grumman has taken significant steps forward to develop the tools needed to assess climate change impacts on public health, collect relevant data for decision making, model projections at regional and local levels; and, deliver information and knowledge to local and regional stakeholders. Supporting these tools is an advanced enterprise architecture consisting of high performance computing, GIS visualization, and standards-based architecture. To address current deficiencies in local planning and decision making with respect to regional climate change and its effect on human health, our research is focused on performing a dynamical downscaling with the Weather Research and Forecasting (WRF) model to develop decision aids that translate the regional climate data into actionable information for users. For the present climate WRF was forced with the Max Planck Institute European Center/Hamburg Model version 5 (ECHAM5) General Circulation Model 20th century simulation. For the 21th century climate, we used an ECHAM5 simulation with the Special Report on Emissions (SRES) A1B emissions scenario. WRF was run in nested mode at spatial resolution of 108 km, 36 km and 12 km and 28 vertical levels. This model was examined relative to two mosquito vectors, both competent carriers of dengue fever, a viral, vector-borne disease. Models which incorporate public health considerations can enable decision makers to take proactive steps to mitigate the impacts and adapt to the changing environmental conditions. In this paper we provide a snapshot of our climate initiative and some examples relative to our public health practice work in vector-borne diseases to illustrate how integrated decision support could be of assistance to regional and local communities worldwide.

Future Midwest Heat Waves in WRF

NASA Astrophysics Data System (ADS)

Huber, M.; Buzan, J. R.; Yoo, J.

2017-12-01

We present heat stress results for the upper Midwest derived from convection resolving Weather Research and Forecasting (WRF) model simulations carried out for the RCP 8.5 Scenario and driven by Community Earth System Model (CESM) boundary conditions as part of the Indiana Climate Change Assessment. Using this modeling system we find widespread and severe increases in moist heat stress metrics in the Midwest by end of century. We detail scaling arguments that suggest our results are robust and not model dependent and describe potential health, welfare, and productivity implications of these results.

Performance of WRF for Simulation of Mesoscale Meteorological Characteristics for Air Quality Assessment over Tropical Coastal City, Chennai

NASA Astrophysics Data System (ADS)

Madala, Srikanth; Srinivas, C. V.; Satyanarayana, A. N. V.

2018-01-01

The land-sea breezes (LSBs) play an important role in transporting air pollution from urban areas on the coast. In this study, the Advanced Research WRF (ARW) mesoscale model is used for predicting boundary layer features to understand the transport of pollution in different seasons over the coastal region of Chennai in Southern India. Sensitivity experiments are conducted with two non-local [Yonsei University (YSU) and Asymmetric Convective Model version 2 (ACM2)] and three turbulence kinetic energy (TKE) closure [Mellor-Yamada-Nakanishi and Niino Level 2.5 (MYNN2) and Mellor-Yamada-Janjic (MYJ) and quasi-normal scale elimination (QNSE)], planetary boundary layer (PBL) parameterization schemes for simulating the thermodynamic structure, and low-level atmospheric flow in different seasons. Comparison of simulations with observations from a global positioning system (GPS) radiosonde, meteorological tower, automated weather stations, and Doppler weather radar (DWR)-derived wind data reveals that the characteristics of LSBs vary widely in different seasons and are more prominent during the pre-monsoon and monsoon seasons (March-September) with large horizontal and vertical extents compared to the post-monsoon and winter seasons. The qualitative and quantitative results indicate that simulations with ACM2 followed by MYNN2 and YSU produced various features of the LSBs, boundary layer parameters and the thermo-dynamical structure in better agreement with observations than other tested physical parameterization schemes. Simulations revealed seasonal variation of onset time, vertical extent of LSBs, and mixed layer depth, which would influence the air pollution dispersion in different seasons over the study region.

Intel Xeon Phi accelerated Weather Research and Forecasting (WRF) Goddard microphysics scheme

NASA Astrophysics Data System (ADS)

Mielikainen, J.; Huang, B.; Huang, A. H.-L.

2014-12-01

The Weather Research and Forecasting (WRF) model is a numerical weather prediction system designed to serve both atmospheric research and operational forecasting needs. The WRF development is a done in collaboration around the globe. Furthermore, the WRF is used by academic atmospheric scientists, weather forecasters at the operational centers and so on. The WRF contains several physics components. The most time consuming one is the microphysics. One microphysics scheme is the Goddard cloud microphysics scheme. It is a sophisticated cloud microphysics scheme in the Weather Research and Forecasting (WRF) model. The Goddard microphysics scheme is very suitable for massively parallel computation as there are no interactions among horizontal grid points. Compared to the earlier microphysics schemes, the Goddard scheme incorporates a large number of improvements. Thus, we have optimized the Goddard scheme code. In this paper, we present our results of optimizing the Goddard microphysics scheme on Intel Many Integrated Core Architecture (MIC) hardware. The Intel Xeon Phi coprocessor is the first product based on Intel MIC architecture, and it consists of up to 61 cores connected by a high performance on-die bidirectional interconnect. The Intel MIC is capable of executing a full operating system and entire programs rather than just kernels as the GPU does. The MIC coprocessor supports all important Intel development tools. Thus, the development environment is one familiar to a vast number of CPU developers. Although, getting a maximum performance out of MICs will require using some novel optimization techniques. Those optimization techniques are discussed in this paper. The results show that the optimizations improved performance of Goddard microphysics scheme on Xeon Phi 7120P by a factor of 4.7×. In addition, the optimizations reduced the Goddard microphysics scheme's share of the total WRF processing time from 20.0 to 7.5%. Furthermore, the same optimizations improved performance on Intel Xeon E5-2670 by a factor of 2.8× compared to the original code.

Evaluation of a regional assimilation system coupled with the WRF-chem model

NASA Astrophysics Data System (ADS)

Liu, Yan-an; Gao, Wei; Huang, Hung-lung; Strabala, Kathleen; Liu, Chaoshun; Shi, Runhe

2013-09-01

Air quality has become a social issue that is causing great concern to humankind across the globe, but particularly in developing countries. Even though the Weather Research and Forecasting with Chemistry (WRF-Chem) model has been applied in many regions, the resolution for inputting meteorology field analysis still impacts the accuracy of forecast. This article describes the application of the CIMSS Regional Assimilation System (CRAS) in East China, and its capability to assimilate the direct broadcast (DB) satellite data for obtaining more detailed meteorological information, including cloud top pressure (CTP) and total precipitation water (TPW) from MODIS. Performance evaluation of CRAS is based on qualitative and quantitative analyses. Compared with data collected from ERA-Interim, Radiosonde, and the Tropical Rainfall Measuring Mission (TRMM) precipitation measurements using bias and Root Mean Square Error (RMSE), CRAS has a systematic error due to the impact of topography and other factors; however, the forecast accuracy of all elements in the model center area is higher at various levels. The bias computed with Radiosonde reveals that the temperature and geopotential height of CRAS are better than ERA-Interim at first guess. Moreover, the location of the 24 h accumulated precipitation forecast are highly consistent with the TRMM retrieval precipitation, which means that the performance of CRAS is excellent. In summation, the newly built Vtable can realize the function of inputting the meteorology field from CRAS output into WRF, which couples the CRAS with WRF-Chem. Therefore, this study not only provides for forecast accuracy of CRAS, but also increases the capability of running the WRF-Chem model at higher resolutions in the future.

A High Resolution Land Cover Data Product to Remove Urban Density Over-Estimation Bias for Coupled Urban-Vegetation-Atmosphere Interaction Studies

NASA Astrophysics Data System (ADS)

Shaffer, S. R.

2017-12-01

Coupled land-atmosphere interactions in urban settings modeled with the Weather Research and Forecasting model (WRF) derive urban land cover from 30-meter resolution National Land Cover Database (NLCD) products. However, within urban areas, the categorical NLCD lose information of non-urban classifications whenever the impervious cover within a grid cell is above 0%, and the current method to determine urban area over estimates the actual area, leading to a bias of urban contribution. To address this bias of urban contribution an investigation is conducted by employing a 1-meter resolution land cover data product derived from the National Agricultural Imagery Program (NAIP) dataset. Scenes during 2010 for the Central Arizona Phoenix Long Term Ecological Research (CAP-LTER) study area, roughly a 120 km x 100 km area containing metropolitan Phoenix, are adapted for use within WRF to determine the areal fraction and urban fraction of each WRF urban class. A method is shown for converting these NAIP data into classes corresponding to NLCD urban classes, and is evaluated in comparison with current WRF implementation using NLCD. Results are shown for comparisons of land cover products at the level of input data and aggregated to model resolution (1 km). The sensitivity of WRF short-term summertime pre-monsoon predictions within metropolitan Phoenix to different input data products of land cover, to method of aggregating these data to model grid scale (1 km), for the default and derived parameter values are examined with the Noah mosaic land surface scheme adapted for using these data. Issues with adapting these non-urban NAIP classes for use in the mosaic approach will also be discussed.

Investigating Lateral Boundary Forcing of Weather Research and Forecasting (WRF) Model Forecasts for Artillery Mission Support

DTIC Science & Technology

2013-01-01

the internal variability, such as the storm track or rainfall pattern (8). Arguments have emerged for the use of small domains in certain cases as...Sensitivity experiments were performed with the WRF-ARW over Meiningen, Germany for two strong wintertime extratropical cyclones. These cases were chosen

Impact of Gas-Phase Mechanisms on Weather Research Forecasting Model with Chemistry (WRF/Chem) Predictions: Mechanism Implementation and Comparative Evaluation

EPA Science Inventory

Gas-phase mechanisms provide important oxidant and gaseous precursors for secondary aerosol formation. Different gas-phase mechanisms may lead to different predictions of gases, aerosols, and aerosol direct and indirect effects. In this study, WRF/Chem-MADRID simulations are cond...

Evaluation of cumulus cloud – radiation interaction effects on air quality –relevant meteorological variables from WRF, from a regional climate perspective

EPA Science Inventory

Aware only of the resolved, grid-scale clouds, the Weather Research & Forecasting model (WRF) does not consider the interactions between subgrid-scale convective clouds and radiation. One consequence of this omission may be WRF’s overestimation of surface precipitation during sum...

Dynamic Evaluation of Two Decades of WRF-CMAQ Ozone Simulations over the Contiguous United States (2017 MAC-MAQ Conference Presentation)

EPA Science Inventory

Dynamic evaluation of two decades of ozone simulations performed with the fully coupled Weather Research and Forecasting (WRF)–Community Multi-scale Air Quality (CMAQ) model over the contiguous United States is conducted to assess how well the changes in observed ozone air ...

Precipitation Retrievals in typhoon domain combining of FY3C MWHTS Observations and WRF Predicted Models

NASA Astrophysics Data System (ADS)

Jieying, HE; Shengwei, ZHANG; Na, LI

2017-02-01

A passive sub-millimeter precipitation retrievals algorithm is provided based on Microwave Humidity and Temperature Sounder (MWHTS) onboard the Chinese Feng Yun 3C (FY-3C) satellite. Using the validated global reference physical model NCEP/WRF/VDISORT), NCEP data per 6 hours are downloaded to run the Weather Research and Forecast model WRF, and derive the typical precipitation data from the whole world. The precipitation retrieval algorithm can operate either on land or on seawater for global. To simply the calculation procedure and save the training time, principle component analysis (PCA) was adapted to filter out the redundancy caused by scanning angle and surface effects, as well as system noise. According to the comparison and validation combing with other precipitation sources, it is demonstrated that the retrievals are reliable for surface precipitation rate higher than 0.1 mm/h at 15km resolution.

The Impact of Infiltration Losses and Model Resolution on the Simulated Hydrometeorological Response of a Semi-Arid Catchment

NASA Astrophysics Data System (ADS)

Mitchell, M. F.; Goodrich, D. C.; Gochis, D. J.; Lahmers, T. M.

2017-12-01

In semi-arid environments with complex terrain, redistribution of moisture occurs through runoff, stream infiltration, and regional groundwater flow. In semi-arid regions, stream infiltration has been shown to account for 10-40% of total recharge in high runoff years. These processes can potentially significantly alter land-atmosphere interactions through changes in sensible and latent heat release. However, currently, their overall impact is still unclear as historical model simulations generally made use of a coarse grid resolution, where these smaller-scale processes were either parameterized or not accounted for. To improve our understanding on the importance of stream infiltration and our ability to represent them in a coupled land-atmosphere model, this study focuses on the Walnut Gulch Experimental Watershed (WGEW) and Long-Term Agro-ecosystem Research (LTAR) site, surrounding the city of Tombstone, AZ. High-resolution surface precipitation, meteorological forcing and distributed runoff measurements have been obtained in WGEW since the 1960s. These data will be used as input for the spatially distributed WRF-Hydro model, a spatially distributed hydrological model that uses the NOAH-MP land surface model. Recently, we have implemented an infiltration loss scheme to WRF-Hydro. We will present the performance of WRF-Hydro to account for stream infiltration by comparing model simulation with in-situ observations. More specifically, as the performance of the model simulations has been shown to depend on the used model grid resolution, in the current work results will present WRF-Hydro simulations obtained at different pixel resolution (10-1000m).

Cross-compartment evaluation of a fully-coupled hydrometeorological modeling system using comprehensive observation data

NASA Astrophysics Data System (ADS)

Fersch, Benjamin; Senatore, Alfonso; Kunstmann, Harald

2017-04-01

Fully-coupled hydrometeorological modeling enables investigations about the complex and often non-linear exchange mechanisms among subsurface, land, and atmosphere with respect to water and energy fluxes. The consideration of lateral redistribution of surface and subsurface water in such modeling systems is a crucial enhancement, allowing for a better representation of surface spatial patterns and providing also channel discharge predictions. However, the evaluation of fully-coupled simulations is difficult since the amount of physical detail along with feedback mechanisms leads to high degrees of freedom. Therefore, comprehensive observation data is required to obtain meaningful model configurations. We present a case study for a medium-sized river catchment in southern Germany that includes the calibration of the stand-alone and the evaluation of the fully-coupled WRF-Hydro modeling system with a horizontal resolution of 1 x 1 km2, for the period June to August 2015. ECMWF ERA-Interim reanalysis is used for model driving. Land-surface processes are represented by the Noah-MP land surface model. Land-cover is described by the EU CORINE data set. Observations for model evaluation are obtained from the TERENO Pre-Alpine observatory (http://www.imk-ifu.kit.edu/tereno.php) and are complemented by further measurements from the ScaleX campaign (http://scalex.imk-ifu.kit.edu) such as atmospheric profiles obtained from radiometer sounding and airborne systems as well as soil moisture and -temperature networks. We show how well water budgets and heat-fluxes are being reproduced by the stand-alone WRF, the stand-alone WRF-Hydro and the fully-coupled WRF-Hydro model.

Weather models as virtual sensors to data-driven rainfall predictions in urban watersheds

NASA Astrophysics Data System (ADS)

Cozzi, Lorenzo; Galelli, Stefano; Pascal, Samuel Jolivet De Marc; Castelletti, Andrea

2013-04-01

Weather and climate predictions are a key element of urban hydrology where they are used to inform water management and assist in flood warning delivering. Indeed, the modelling of the very fast dynamics of urbanized catchments can be substantially improved by the use of weather/rainfall predictions. For example, in Singapore Marina Reservoir catchment runoff processes have a very short time of concentration (roughly one hour) and observational data are thus nearly useless for runoff predictions and weather prediction are required. Unfortunately, radar nowcasting methods do not allow to carrying out long - term weather predictions, whereas numerical models are limited by their coarse spatial scale. Moreover, numerical models are usually poorly reliable because of the fast motion and limited spatial extension of rainfall events. In this study we investigate the combined use of data-driven modelling techniques and weather variables observed/simulated with a numerical model as a way to improve rainfall prediction accuracy and lead time in the Singapore metropolitan area. To explore the feasibility of the approach, we use a Weather Research and Forecast (WRF) model as a virtual sensor network for the input variables (the states of the WRF model) to a machine learning rainfall prediction model. More precisely, we combine an input variable selection method and a non-parametric tree-based model to characterize the empirical relation between the rainfall measured at the catchment level and all possible weather input variables provided by WRF model. We explore different lead time to evaluate the model reliability for different long - term predictions, as well as different time lags to see how past information could improve results. Results show that the proposed approach allow a significant improvement of the prediction accuracy of the WRF model on the Singapore urban area.

Comparison of thunderstorm simulations from WRF-NMM and WRF-ARW models over East Indian Region.

PubMed

Litta, A J; Mary Ididcula, Sumam; Mohanty, U C; Kiran Prasad, S

2012-01-01

The thunderstorms are typical mesoscale systems dominated by intense convection. Mesoscale models are essential for the accurate prediction of such high-impact weather events. In the present study, an attempt has been made to compare the simulated results of three thunderstorm events using NMM and ARW model core of WRF system and validated the model results with observations. Both models performed well in capturing stability indices which are indicators of severe convective activity. Comparison of model-simulated radar reflectivity imageries with observations revealed that NMM model has simulated well the propagation of the squall line, while the squall line movement was slow in ARW. From the model-simulated spatial plots of cloud top temperature, we can see that NMM model has better captured the genesis, intensification, and propagation of thunder squall than ARW model. The statistical analysis of rainfall indicates the better performance of NMM than ARW. Comparison of model-simulated thunderstorm affected parameters with that of the observed showed that NMM has performed better than ARW in capturing the sharp rise in humidity and drop in temperature. This suggests that NMM model has the potential to provide unique and valuable information for severe thunderstorm forecasters over east Indian region.

Worsening of Renal Function During 1 Year After Hospital Discharge Is a Strong and Independent Predictor of All‐Cause Mortality in Acute Decompensated Heart Failure

PubMed Central

Ueda, Tomoya; Kawakami, Rika; Sugawara, Yu; Okada, Sadanori; Nishida, Taku; Onoue, Kenji; Soeda, Tsunenari; Okayama, Satoshi; Takeda, Yukiji; Watanabe, Makoto; Kawata, Hiroyuki; Uemura, Shiro; Saito, Yoshihiko

2014-01-01

Background Renal impairment is a common comorbidity and the strongest risk factor for poor prognosis in acute decompensated heart failure (ADHF). In clinical practice, renal function is labile during episodes of ADHF, and often worsens after discharge. The significance of worsening of renal function (WRF) after discharge has not been investigated as extensively as baseline renal function at admission or WRF during hospitalization. Methods and Results Among 611 consecutive patients with ADHF emergently admitted to our hospital, 233 patients with 3 measurements of serum creatinine (SCr) level measurements (on admission, at discharge, and 1 year after discharge) were included in the present study. Patients were divided into 2 groups according to the presence or absence of WRF at 1 year after discharge (1y‐WRF), defined as an absolute increase in SCr >0.3 mg/dL (>26.5 μmol/L) plus a ≥25% increase in SCr at 1 year after discharge compared to the SCr value at discharge. All‐cause and cardiovascular mortality were assessed as adverse outcomes. During a mean follow‐up of 35.4 months, 1y‐WRF occurred in 48 of 233 patients. There were 66 deaths from all causes. All‐cause and cardiovascular mortality were significantly higher in patients with 1y‐WRF (log‐rank P<0.0001 and P<0.0001, respectively) according to Kaplan–Meier analysis. In a multivariate Cox proportional hazards model, 1y‐WRF was a strong and independent predictor of all‐cause and cardiovascular mortality. Hemoglobin and B‐type natriuretic peptide at discharge, as well as left ventricular ejection fraction <50%, were independent predictors of 1y‐WRF. Conclusions In patients with ADHF, 1y‐WRF is a strong predictor of all‐cause and cardiovascular mortality. PMID:25370599

Worsening of renal function during 1 year after hospital discharge is a strong and independent predictor of all-cause mortality in acute decompensated heart failure.

PubMed

Ueda, Tomoya; Kawakami, Rika; Sugawara, Yu; Okada, Sadanori; Nishida, Taku; Onoue, Kenji; Soeda, Tsunenari; Okayama, Satoshi; Takeda, Yukiji; Watanabe, Makoto; Kawata, Hiroyuki; Uemura, Shiro; Saito, Yoshihiko

2014-11-04

Renal impairment is a common comorbidity and the strongest risk factor for poor prognosis in acute decompensated heart failure (ADHF). In clinical practice, renal function is labile during episodes of ADHF, and often worsens after discharge. The significance of worsening of renal function (WRF) after discharge has not been investigated as extensively as baseline renal function at admission or WRF during hospitalization. Among 611 consecutive patients with ADHF emergently admitted to our hospital, 233 patients with 3 measurements of serum creatinine (SCr) level measurements (on admission, at discharge, and 1 year after discharge) were included in the present study. Patients were divided into 2 groups according to the presence or absence of WRF at 1 year after discharge (1y-WRF), defined as an absolute increase in SCr >0.3 mg/dL (>26.5 μmol/L) plus a ≥25% increase in SCr at 1 year after discharge compared to the SCr value at discharge. All-cause and cardiovascular mortality were assessed as adverse outcomes. During a mean follow-up of 35.4 months, 1y-WRF occurred in 48 of 233 patients. There were 66 deaths from all causes. All-cause and cardiovascular mortality were significantly higher in patients with 1y-WRF (log-rank P<0.0001 and P<0.0001, respectively) according to Kaplan-Meier analysis. In a multivariate Cox proportional hazards model, 1y-WRF was a strong and independent predictor of all-cause and cardiovascular mortality. Hemoglobin and B-type natriuretic peptide at discharge, as well as left ventricular ejection fraction <50%, were independent predictors of 1y-WRF. In patients with ADHF, 1y-WRF is a strong predictor of all-cause and cardiovascular mortality. © 2014 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

An investigation of methods for injecting emissions from boreal wildfires using WRF-Chem during ARCTAS

NASA Astrophysics Data System (ADS)

Sessions, W. R.; Fuelberg, H. E.; Kahn, R. A.; Winker, D. M.

2010-11-01

The Weather Research and Forecasting Model (WRF) is considered a "next generation" mesoscale meteorology model. The inclusion of a chemistry module (WRF-Chem) allows transport simulations of chemical and aerosol species such as those observed during NASA's Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) in 2008. The ARCTAS summer deployment phase during June and July coincided with large boreal wildfires in Saskatchewan and Eastern Russia. One of the most important aspects of simulating wildfire plume transport is the height at which emissions are injected. WRF-Chem contains an integrated one-dimensional plume rise model to determine the appropriate injection layer. The plume rise model accounts for thermal buoyancy associated with fires and the local atmospheric stability. This study compares results from the plume model against those of two more traditional injection methods: Injecting within the planetary boundary layer, and in a layer 3-5 km above ground level. Fire locations are satellite derived from the GOES Wildfire Automated Biomass Burning Algorithm (WF_ABBA) and the MODIS thermal hotspot detection. Two methods for preprocessing these fire data are compared: The prep_chem_sources method included with WRF-Chem, and the Naval Research Laboratory's Fire Locating and Monitoring of Burning Emissions (FLAMBE). Results from the simulations are compared with satellite-derived products from the AIRS, MISR and CALIOP sensors. Results show that the FLAMBE pre-processor produces more realistic injection heights than does prep_chem_sources. The plume rise model using FLAMBE provides the best agreement with satellite-observed injection heights. Conversely, when the planetary boundary layer or the 3-5 km AGL layer were filled with emissions, the resulting injection heights exhibit less agreement with observed plume heights. Results indicate that differences in injection heights produce different transport pathways. These differences are especially pronounced in areas of strong vertical wind shear and when the integration period is long.

Precipitation forecast verification over Brazilian watersheds on present and future climate

NASA Astrophysics Data System (ADS)

Xavier, L.; Bruyere, C. L.; Rotunno, O.

2016-12-01

Evaluating the quality of precipitation forecast is an essential step for hydrological studies, among other applications, which is particularly relevant when taking into account climate change and the consequent likely modification of precipitation patterns. In this study we analyzed daily precipitation forecasts given by the global model CESM and the regional model WRF on present and future climate. For present runs, CESM data have been considered from 1980 to 2005, and WRF data from 1990 to 2000. CESM future runs were available for 3 RCP scenarios (4.5, 6.0 and 8.5), over 2005-2100 period; for WRF, future runs spanned 4 different 11-year periods (2020-2030, 2030-2040, 2050-2060 and 2080-2090). WRF simulations had been driven by bias-corrected forcings, and had been done on present climate for a 24 members ensemble created by varying the adopted parameterization schemes. On WRF future climate simulations, data from 3 members out of the original ensemble were available. Precipitation data have been spatially averaged over some large Brazilian watersheds (Amazon and subbasins, Tocantins, Sao Francisco, 4 of Parana`s subbasins) and have been evaluated for present climate against a gauge gridded dataset and ERA Interim data both spanning the 1980-2013 period. The evaluation was focused on the analysis of precipitation forecasts probabilities distribution. Taking into account daily and monthly mean precipitation aggregated on 3-month periods (DJF,MAM,JJA,SON), we adopted some skill measures, amongst them, the Perkins Skill Score (PSS). From the results we verified that on present climate WRF ensemble mean led to clearly better results when compared with CESM data for Amazon, Tocantins and Sao Francisco, but model was not as skillful to the other basins, which could be also been observed for future climate. PSS results from future runs showed that few changes would be observed over the different periods for the considered basins.

Extreme Rainfall from Hurricane Harvey (2017): Intercomparisons of WRF Simulations and Polarimetric Radar Fields

NASA Astrophysics Data System (ADS)

Yang, L.; Smith, J. A.; Liu, M.; Baeck, M. L.; Chaney, M. M.; Su, Y.

2017-12-01

Hurricane Harvey made landfall on 25 August 2017 and produced more than a meter of rain during a four-day period over eastern Texas, making it the wettest tropical cyclone on record in the United States. Extreme rainfall from Harvey was predominantly related to the dynamics and structure of outer rain bands. In this study, we provide details of the extreme rainfall produced by Hurricane Harvey. The principal research questions that motivate this study are: (1) what are the key microphysical properties of extreme rainfall from landfalling tropical cyclones and (2) what are the capabilities and deficiencies of existing bulk microphysics parameterizations from the physical models in capturing them. Our analyses are centered on intercomparisons of high-resolution simulations using the Weather Research and Forecasting (WRF) model and polarimetric radar fields from KHGX (Houston, Texas) WSR-88D. The WRF simulations accurately capture the track and intensity of Hurricane Harvey. Multi-rainband structure and its key evolution features are also well represented in the simulations. Two microphysics parameterizations (WSM6 and WDM6) are tested in this study. Radar reflectivity and differential reflectivity fields simulated by the WRF model are compared with polarimetric radar observations. An important feature for the extreme rainfall from Hurricane Harvey is the sharp boundary of spatial rainfall accumulation along the coast (with torrential rainfall distributed over Houston and its surrounding inland areas). We will examine the role of land-sea contrasts in dictating storm structure and evolution from both WRF simulations and polarimetric radar fields. Implications for improving hurricane rainfall forecasts and estimates will be provided.

Sensitivity of WRF-chem predictions to dust source function specification in West Asia

NASA Astrophysics Data System (ADS)

Nabavi, Seyed Omid; Haimberger, Leopold; Samimi, Cyrus

2017-02-01

Dust storms tend to form in sparsely populated areas covered by only few observations. Dust source maps, known as source functions, are used in dust models to allocate a certain potential of dust release to each place. Recent research showed that the well known Ginoux source function (GSF), currently used in Weather Research and Forecasting Model coupled with Chemistry (WRF-chem), exhibits large errors over some regions in West Asia, particularly near the IRAQ/Syrian border. This study aims to improve the specification of this critical part of dust forecasts. A new source function based on multi-year analysis of satellite observations, called West Asia source function (WASF), is therefore proposed to raise the quality of WRF-chem predictions in the region. WASF has been implemented in three dust schemes of WRF-chem. Remotely sensed and ground-based observations have been used to verify the horizontal and vertical extent and location of simulated dust clouds. Results indicate that WRF-chem performance is significantly improved in many areas after the implementation of WASF. The modified runs (long term simulations over the summers 2008-2012, using nudging) have yielded an average increase of Spearman correlation between observed and forecast aerosol optical thickness by 12-16 percent points compared to control runs with standard source functions. They even outperform MACC and DREAM dust simulations over many dust source regions. However, the quality of the forecasts decreased with distance from sources, probably due to deficiencies in the transport and deposition characteristics of the forecast model in these areas.

Evaluation of a data fusion approach to estimate daily PM2.5 levels in North China

PubMed Central

Liang, Fengchao; Gao, Meng; Xiao, Qingyang; Carmichael, Gregory R.

2017-01-01

PM2.5 air pollution has been a growing concern worldwide. Previous studies have conducted several techniques to estimate PM2.5 exposure spatiotemporally in China, but all these have limitations. This study was to develop a data fusion approach and compare it with kriging and Chemistry Module. Two techniques were applied to create daily spatial cover of PM2.5 in grid cells with a resolution of 10 km in North China in 2013, respectively, which was kriging with an external drift (KED) and Weather Research and Forecast Model with Chemistry Module (WRF-Chem). A data fusion technique was developed by fusing PM2.5 concentration predicted by KED and WRF-Chem, accounting for the distance from the central of grid cell to the nearest ground observations and daily spatial correlations between WRF-Chem and observations. Model performances were evaluated by comparing them with ground observations and the spatial prediction errors. KED and data fusion performed better at monitoring sites with a daily model R2 of 0.95 and 0.94, respectively and PM2.5 was overestimated by WRF-Chem (R2=0.51). KED and data fusion performed better around the ground monitors, WRF-Chem performed relative worse with high prediction errors in the central of study domain. In our study, both KED and data fusion technique provided highly accurate PM2.5. Current monitoring network in North China was dense enough to provide a reliable PM2.5 prediction by interpolation technique. PMID:28599195

Evaluation of a data fusion approach to estimate daily PM2.5 levels in North China.

PubMed

Liang, Fengchao; Gao, Meng; Xiao, Qingyang; Carmichael, Gregory R; Pan, Xiaochuan; Liu, Yang

2017-10-01

PM 2.5 air pollution has been a growing concern worldwide. Previous studies have conducted several techniques to estimate PM 2.5 exposure spatiotemporally in China, but all these have limitations. This study was to develop a data fusion approach and compare it with kriging and Chemistry Module. Two techniques were applied to create daily spatial cover of PM 2.5 in grid cells with a resolution of 10km in North China in 2013, respectively, which was kriging with an external drift (KED) and Weather Research and Forecast Model with Chemistry Module (WRF-Chem). A data fusion technique was developed by fusing PM 2.5 concentration predicted by KED and WRF-Chem, accounting for the distance from the central of grid cell to the nearest ground observations and daily spatial correlations between WRF-Chem and observations. Model performances were evaluated by comparing them with ground observations and the spatial prediction errors. KED and data fusion performed better at monitoring sites with a daily model R 2 of 0.95 and 0.94, respectively and PM 2.5 was overestimated by WRF-Chem (R 2 =0.51). KED and data fusion performed better around the ground monitors, WRF-Chem performed relative worse with high prediction errors in the central of study domain. In our study, both KED and data fusion technique provided highly accurate PM 2.5 . Current monitoring network in North China was dense enough to provide a reliable PM 2.5 prediction by interpolation technique. Copyright © 2017. Published by Elsevier Inc.

Evaluation of the WRF-Urban Modeling System Coupled to Noah and Noah-MP Land Surface Models Over a Semiarid Urban Environment

NASA Astrophysics Data System (ADS)

Salamanca, Francisco; Zhang, Yizhou; Barlage, Michael; Chen, Fei; Mahalov, Alex; Miao, Shiguang

2018-03-01

We have augmented the existing capabilities of the integrated Weather Research and Forecasting (WRF)-urban modeling system by coupling three urban canopy models (UCMs) available in the WRF model with the new community Noah with multiparameterization options (Noah-MP) land surface model (LSM). The WRF-urban modeling system's performance has been evaluated by conducting six numerical experiments at high spatial resolution (1 km horizontal grid spacing) during a 15 day clear-sky summertime period for a semiarid urban environment. To assess the relative importance of representing urban surfaces, three different urban parameterizations are used with the Noah and Noah-MP LSMs, respectively, over the two major cities of Arizona: Phoenix and Tucson metropolitan areas. Our results demonstrate that Noah-MP reproduces somewhat better than Noah the daily evolution of surface skin temperature and near-surface air temperature (especially nighttime temperature) and wind speed. Concerning the urban areas, bulk urban parameterization overestimates nighttime 2 m air temperature compared to the single-layer and multilayer UCMs that reproduce more accurately the daily evolution of near-surface air temperature. Regarding near-surface wind speed, only the multilayer UCM was able to reproduce realistically the daily evolution of wind speed, although maximum winds were slightly overestimated, while both the single-layer and bulk urban parameterizations overestimated wind speed considerably. Based on these results, this paper demonstrates that the new community Noah-MP LSM coupled to an UCM is a promising physics-based predictive modeling tool for urban applications.

Evaluation of Extratropical Cyclone Precipitation in the North Atlantic Basin: An analysis of ERA-Interim, WRF, and two CMIP5 models.

PubMed

Booth, James F; Naud, Catherine M; Willison, Jeff

2018-03-01

The representation of extratropical cyclones (ETCs) precipitation in general circulation models (GCMs) and a weather research and forecasting (WRF) model is analyzed. This work considers the link between ETC precipitation and dynamical strength and tests if parameterized convection affects this link for ETCs in the North Atlantic Basin. Lagrangian cyclone tracks of ETCs in ERA-Interim reanalysis (ERAI), the GISS and GFDL CMIP5 models, and WRF with two horizontal resolutions are utilized in a compositing analysis. The 20-km resolution WRF model generates stronger ETCs based on surface wind speed and cyclone precipitation. The GCMs and ERAI generate similar composite means and distributions for cyclone precipitation rates, but GCMs generate weaker cyclone surface winds than ERAI. The amount of cyclone precipitation generated by the convection scheme differs significantly across the datasets, with GISS generating the most, followed by ERAI and then GFDL. The models and reanalysis generate relatively more parameterized convective precipitation when the total cyclone-averaged precipitation is smaller. This is partially due to the contribution of parameterized convective precipitation occurring more often late in the ETC life cycle. For reanalysis and models, precipitation increases with both cyclone moisture and surface wind speed, and this is true if the contribution from the parameterized convection scheme is larger or not. This work shows that these different models generate similar total ETC precipitation despite large differences in the parameterized convection, and these differences do not cause unexpected behavior in ETC precipitation sensitivity to cyclone moisture or surface wind speed.

The diagnosis of severe thunderstorms with high-resolution WRF model

NASA Astrophysics Data System (ADS)

Litta, A. J.; Mohanty, U. C.; Idicula, Sumam Mary

2012-04-01

Thunderstorm, resulting from vigorous convective activity, is one of the most spectacular weather phenomena in the atmosphere. A common feature of the weather during the pre-monsoon season over the Indo-Gangetic Plain and northeast India is the outburst of severe local convective storms, commonly known as `Nor'westers'(as they move from northwest to southeast). The severe thunderstorms associated with thunder, squall lines, lightning and hail cause extensive losses in agricultural, damage to structure and also loss of life. In this paper, sensitivity experiments have been conducted with the Non-hydrostatic Mesoscale Model (NMM) to test the impact of three microphysical schemes in capturing the severe thunderstorm event occurred over Kolkata on 15 May 2009. The results show that the WRF-NMM model with Ferrier microphysical scheme appears to reproduce the cloud and precipitation processes more realistically than other schemes. Also, we have made an attempt to diagnose four severe thunderstorms that occurred during pre-monsoon seasons of 2006, 2007 and 2008 through the simulated radar reflectivity fields from NMM model with Ferrier microphysics scheme and validated the model results with Kolkata Doppler Weather Radar (DWR) observations. Composite radar reflectivity simulated by WRF-NMM model clearly shows the severe thunderstorm movement as observed by DWR imageries, but failed to capture the intensity as in observations. The results of these analyses demonstrated the capability of high resolution WRF-NMM model in the simulation of severe thunderstorm events and determined that the 3 km model improve upon current abilities when it comes to simulating severe thunderstorms over east Indian region.

Multi-year downscaling application of two-way coupled WRF v3.4 and CMAQ v5.0.2 over east Asia for regional climate and air quality modeling: model evaluation and aerosol direct effects

NASA Astrophysics Data System (ADS)

Hong, Chaopeng; Zhang, Qiang; Zhang, Yang; Tang, Youhua; Tong, Daniel; He, Kebin

2017-06-01

In this study, a regional coupled climate-chemistry modeling system using the dynamical downscaling technique was established by linking the global Community Earth System Model (CESM) and the regional two-way coupled Weather Research and Forecasting - Community Multi-scale Air Quality (WRF-CMAQ) model for the purpose of comprehensive assessments of regional climate change and air quality and their interactions within one modeling framework. The modeling system was applied over east Asia for a multi-year climatological application during 2006-2010, driven with CESM downscaling data under Representative Concentration Pathways 4.5 (RCP4.5), along with a short-term air quality application in representative months in 2013 that was driven with a reanalysis dataset. A comprehensive model evaluation was conducted against observations from surface networks and satellite observations to assess the model's performance. This study presents the first application and evaluation of the two-way coupled WRF-CMAQ model for climatological simulations using the dynamical downscaling technique. The model was able to satisfactorily predict major meteorological variables. The improved statistical performance for the 2 m temperature (T2) in this study (with a mean bias of -0.6 °C) compared with the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-models might be related to the use of the regional model WRF and the bias-correction technique applied for CESM downscaling. The model showed good ability to predict PM2. 5 in winter (with a normalized mean bias (NMB) of 6.4 % in 2013) and O3 in summer (with an NMB of 18.2 % in 2013) in terms of statistical performance and spatial distributions. Compared with global models that tend to underpredict PM2. 5 concentrations in China, WRF-CMAQ was able to capture the high PM2. 5 concentrations in urban areas. In general, the two-way coupled WRF-CMAQ model performed well for both climatological and air quality applications. The coupled modeling system with direct aerosol feedbacks predicted aerosol optical depth relatively well and significantly reduced the overprediction in downward shortwave radiation at the surface (SWDOWN) over polluted regions in China. The performance of cloud variables was not as good as other meteorological variables, and underpredictions of cloud fraction resulted in overpredictions of SWDOWN and underpredictions of shortwave and longwave cloud forcing. The importance of climate-chemistry interactions was demonstrated via the impacts of aerosol direct effects on climate and air quality. The aerosol effects on climate and air quality in east Asia (e.g., SWDOWN and T2 decreased by 21.8 W m-2 and 0.45 °C, respectively, and most pollutant concentrations increased by 4.8-9.5 % in January over China's major cities) were more significant than in other regions because of higher aerosol loadings that resulted from severe regional pollution, which indicates the need for applying online-coupled models over east Asia for regional climate and air quality modeling and to study the important climate-chemistry interactions. This work established a baseline for WRF-CMAQ simulations for a future period under the RCP4.5 climate scenario, which will be presented in a future paper.

Application of WRF/Chem-MADRID and WRF/Polyphemus in Europe - Part 1: Model description, evaluation of meteorological predictions, and aerosol-meteorology interactions

NASA Astrophysics Data System (ADS)

Zhang, Y.; Sartelet, K.; Wu, S.-Y.; Seigneur, C.

2013-07-01

Comprehensive model evaluation and comparison of two 3-D air quality modeling systems (i.e., the Weather Research and Forecast model (WRF)/Polyphemus and WRF with chemistry and the Model of Aerosol Dynamics, Reaction, Ionization, and Dissolution (MADRID) (WRF/Chem-MADRID)) are conducted over Western Europe. Part 1 describes the background information for the model comparison and simulation design, the application of WRF for January and July 2001 over triple-nested domains in Western Europe at three horizontal grid resolutions: 0.5°, 0.125°, and 0.025°, and the effect of aerosol/meteorology interactions on meteorological predictions. Nine simulated meteorological variables (i.e., downward shortwave and longwave radiation fluxes (SWDOWN and LWDOWN), outgoing longwave radiation flux (OLR), temperature at 2 m (T2), specific humidity at 2 m (Q2), relative humidity at 2 m (RH2), wind speed at 10 m (WS10), wind direction at 10 m (WD10), and precipitation (Precip)) are evaluated using available observations in terms of spatial distribution, domainwide daily and site-specific hourly variations, and domainwide performance statistics. The vertical profiles of temperature, dew points, and wind speed/direction are also evaluated using sounding data. WRF demonstrates its capability in capturing diurnal/seasonal variations and spatial gradients and vertical profiles of major meteorological variables. While the domainwide performance of LWDOWN, OLR, T2, Q2, and RH2 at all three grid resolutions is satisfactory overall, large positive or negative biases occur in SWDOWN, WS10, and Precip even at 0.125° or 0.025° in both months and in WD10 in January. In addition, discrepancies between simulations and observations exist in T2, Q2, WS10, and Precip at mountain/high altitude sites and large urban center sites in both months, in particular, during snow events or thunderstorms. These results indicate the model's difficulty in capturing meteorological variables in complex terrain and subgrid-scale meteorological phenomena, due to inaccuracies in model initialization parameterization (e.g., lack of soil temperature and moisture nudging), limitations in the physical parameterizations (e.g., shortwave radiation, cloud microphysics, cumulus parameterizations, and ice nucleation treatments) as well as limitations in surface heat and moisture budget parameterizations (e.g., snow-related processes, subgrid-scale surface roughness elements, and urban canopy/heat island treatments and CO2 domes). While the use of finer grid resolutions of 0.125° and 0.025° shows some improvements for WS10, WD10, Precip, and some mesoscale events (e.g., strong forced convection and heavy precipitation), it does not significantly improve the overall statistical performance for all meteorological variables except for Precip. The WRF/Chem simulations with and without aerosols show that aerosols lead to reduced net shortwave radiation fluxes, 2 m temperature, 10 m wind speed, planetary boundary layer (PBL) height, and precipitation and increase aerosol optical depth, cloud condensation nuclei, cloud optical depth, and cloud droplet number concentrations over most of the domain. These results indicate a need to further improve the model representations of the above parameterizations as well as aerosol-meteorology interactions at all scales.

The Comparison of Point Data Models for the Output of WRF Hydro Model in the IDV

NASA Astrophysics Data System (ADS)

Ho, Y.; Weber, J.

2017-12-01

WRF Hydro netCDF output files contain streamflow, flow depth, longitude, latitude, altitude and stream order values for each forecast point. However, the data are not CF compliant. The total number of forecast points for the US CONUS is approximately 2.7 million and it is a big challenge for any visualization and analysis tool. The IDV point cloud display shows point data as a set of points colored by parameter. This display is very efficient compared to a standard point type display for rendering a large number of points. The one problem we have is that the data I/O can be a bottleneck issue when dealing with a large collection of point input files. In this presentation, we will experiment with different point data models and their APIs to access the same WRF Hydro model output. The results will help us construct a CF compliant netCDF point data format for the community.

Diagnosing the Nature of Land-Atmosphere Coupling During the 2006-7 Dry/Wet Extremes in the U. S. Southern Great Plains

NASA Technical Reports Server (NTRS)

Santanello, Joseph A., Jr.; Peters-Lidard, Christa D.; Kumar, Sujay V.; Dong, Xiquan; Kennedy, Aaron D.

2011-01-01

The degree of coupling between the land surface and PBL in NWP models remains largely undiagnosed due to the complex interactions and feedbacks present across a range of scales. In this study, a framework for diagnosing local land-atmosphere coupling (LoCo) is presented using a coupled mesoscale model with observations during the summers of 2006/7 in the U.S. Southern Great Plains. Specifically, the Weather Research and Forecasting (WRF) model has been coupled to NASA's Land Information System (LIS), which enables a suite of PBL and land surface model (LSM) options along provides a flexible and high-resolution representation and initialization of land surface physics and states. This coupling is one component of a larger project to develop a NASA-Unified WRF (NU-WRF) system. A range of diagnostics exploring the feedbacks between soil moisture and precipitation are examined for the dry/wet extremes, along with the sensitivity of PBL-LSM coupling to perturbations in soil moisture.

The prognostic impact of worsening renal function in Japanese patients undergoing percutaneous coronary intervention with acute coronary syndrome.

PubMed

Murata, Nobuhiro; Kaneko, Hidehiro; Yajima, Junji; Oikawa, Yuji; Oshima, Toru; Tanaka, Shingo; Kano, Hiroto; Matsuno, Shunsuke; Suzuki, Shinya; Kato, Yuko; Otsuka, Takayuki; Uejima, Tokuhisa; Nagashima, Kazuyuki; Kirigaya, Hajime; Sagara, Koichi; Sawada, Hitoshi; Aizawa, Tadanori; Yamashita, Takeshi

2015-10-01

The prognostic impact of worsening renal function (WRF) in acute coronary syndrome (ACS) patients is not fully understood in Japanese clinical practice, and clinical implication of persistent versus transient WRF in ACS patients is also unclear. With a single hospital-based cohort in the Shinken database 2004-2012 (n=19,994), we followed 604 ACS patients who underwent percutaneous coronary intervention (PCI). WRF was defined as an increase in creatinine during hospitalization of ≥0.3mg/dl above admission value. Persistent WRF was defined as an increase in creatinine during hospitalization of ≥0.3mg/dl above admission value and maintained until discharge, whereas transient WRF was defined as that WRF resolved at hospital discharge. WRF occurred in 78 patients (13%), persistent WRF 35 patients (6%) and transient WRF 43 patients (7%). WRF patients were older and had a higher prevalence of chronic kidney disease, history of myocardial infarction (MI), and ST elevation MI. WRF was associated with elevated inflammatory markers and reduced left ventricular (LV) ejection fraction in acute, chronic phase. Incidence of all-cause death and major adverse cardiac events (MACE: all-cause death, MI, and target lesion revascularization) was significantly higher in patients with WRF. Moreover, in the WRF group, incidences of all-cause death and MACE were higher in patients with persistent WRF than those with transient WRF. A multivariate analysis showed that as well as older age, female gender, and intubation, WRF was an independent determinant of the all-cause death in ACS patients who underwent PCI. In conclusion, WRF might have a prognostic impact among Japanese ACS patients who underwent PCI in association with enhanced inflammatory response and LV remodeling. Persistent WRF might portend increased events, while transient WRF might have association with favorable outcomes compared with persistent WRF. Copyright © 2014 Japanese College of Cardiology. Published by Elsevier Ltd. All rights reserved.

Collaborative Project: Understanding Climate Model Biases in Tropical Atlantic and Their Impact on Simulations of Extreme Climate Events

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chang, Ping

Recent studies have revealed that among all the tropical oceans, the tropical Atlantic has experienced the most pronounced warming trend over the 20th century. Many extreme climate events affecting the U.S., such as hurricanes, severe precipitation and drought events, are influenced by conditions in the Gulf of Mexico and the Atlantic Ocean. It is therefore imperative to have accurate simulations of the climatic mean and variability in the Atlantic region to be able to make credible projections of future climate change affecting the U.S. and other countries adjoining the Atlantic Ocean. Unfortunately, almost all global climate models exhibit large biasesmore » in their simulations of tropical Atlantic climate. The atmospheric convection simulation errors in the Amazon region and the associated errors in the trade wind simulations are hypothesized to be a leading cause of the tropical Atlantic biases in climate models. As global climate models have resolutions that are too coarse to resolve some of the atmospheric and oceanic processes responsible for the model biases, we propose to use a high-resolution coupled regional climate model (CRCM) framework to address the tropical bias issue. We propose to combine the expertise in tropical coupled atmosphere-ocean modeling at Texas A&M University (TAMU) and the coupled land-atmosphere modeling expertise at Pacific Northwest National Laboratory (PNNL) to develop a comprehensive CRCM for the Atlantic sector within a general and flexible modeling framework. The atmospheric component of the CRCM will be the NCAR WRF model and the oceanic component will be the Rutgers/UCLA ROMS. For the land component, we will use CLM modified at PNNL to include more detailed representations of vegetation and soil hydrology processes. The combined TAMU-PNNL CRCM model will be used to simulate the Atlantic climate, and the associated land-atmosphere-ocean interactions at a horizontal resolution of 9 km or finer. A particular focus of the model development effort will be to optimize the performance of WRF and ROMS over several thousand of cores by focusing on both the parallel communication libraries and the I/O interfaces, in order to achieve the sustained throughput needed to perform simulations on such fine resolution grids. The CRCM model will be developed within the framework of the Coupler (CPL7) software that is part of the NCAR Community Earth System Model (CESM). Through efforts at PNNL and within the community, WRF and CLM have already been coupled via CPL7. Using the flux coupler approach for the whole CRCM model will allow us to flexibly couple WRF, ROMS, and CLM with each model running on its own grid at different resolutions. In addition, this framework will allow us to easily port parameterizations between CESM and the CRCM, and potentially allow partial coupling between the CESM and the CRCM. TAMU and PNNL will contribute cooperatively to this research endeavor. The TAMU team led by Chang and Saravanan has considerable experience in studying atmosphere-ocean interactions within tropical Atlantic sector and will focus on modeling issues that relate to coupling WRF and ROMS. The PNNL team led by Leung has extensive expertise in atmosphere-land interaction and will be responsible for improving the land surface parameterization. Both teams will jointly work on integrating WRF-ROMS and WRF-CLM to couple WRF, ROMS, and CLM through CPL7. Montuoro of the TAMU Supercomputing Center will be responsible for improving the MPI and Parallel IO interfaces of the CRCM. Both teams will contribute to the design and execution of the proposed numerical experiments and jointly perform analysis of the numerical experiments.« less

BaP (PAH) air quality modelling exercise over Zaragoza (Spain) using an adapted version of WRF-CMAQ model.

PubMed

San José, Roberto; Pérez, Juan Luis; Callén, María Soledad; López, José Manuel; Mastral, Ana

2013-12-01

Benzo(a)pyrene (BaP) is one of the most dangerous PAH due to its high carcinogenic and mutagenic character. Because of this reason, the Directive 2004/107/CE of the European Union establishes a target value of 1 ng/m(3) of BaP in the atmosphere. In this paper, the main aim is to estimate the BaP concentrations in the atmosphere by using last generation of air quality dispersion models with the inclusion of the transport, scavenging and deposition processes for the BaP. The degradation of the particulated BaP by the ozone has been considered. The aerosol-gas partitioning phenomenon in the atmosphere is modelled taking into a count that the concentrations in the gas and the aerosol phases. If the pre-existing organic aerosol concentrations are zero gas/particle equilibrium is established. The model has been validated at local scale with data from a sampling campaign carried out in the area of Zaragoza (Spain) during 12 weeks. Copyright © 2013 Elsevier Ltd. All rights reserved.

Worsening renal function is not associated with response to treatment in acute heart failure

PubMed Central

Ather, Sameer; Bavishi, Chirag; McCauley, Mark D; Dhaliwal, Amandeep; Deswal, Anita; Johnson, Sarah; Chan, Wenyaw; Aguilar, David; Pritchett, Allison M; Ramasubbu, Kumudha; Wehrens, Xander HT; Bozkurt, Biykem

2015-01-01

Background About a fourth of acute decompensated heart failure (ADHF) patients develop renal dysfunction during their admission. To date, the association of ADHF treatment with the development of worsening renal function (WRF) remains contentious. Thus, we examined the association of WRF with changes in BNP levels and with mortality. Methods We performed retrospective chart review of patients admitted with ADHF who had BNP, eGFR, creatinine and blood urea nitrogen (BUN) values measured both on admission and discharge. Survival analysis was conducted using Cox proportional hazards model and correlation was measured using Spearman's rank correlation test. Results 358 patients admitted for ADHF were evaluated. WRF was defined as >20% reduction in eGFR from admission to discharge and response to treatment was assessed by ΔBNP. There was a statistically significant reduction in BNP and increase in BUN during the admission. ΔBNP did not correlate with either ΔGFR or ΔBUN. Patients who developed WRF and those who did not, had a similar reduction in BNP. On univariate survival analysis, ΔBUN, but not ΔeGFR, was associated with 1-year mortality. In multivariate Cox proportional hazards model, BUN at discharge was associated with 1-year mortality (HR: 1.02, p<0.001), but ΔeGFR and ΔBUN were not associated with the primary endpoint. Conclusion During ADHF treatment, ΔBNP was not associated with changes in renal function. Development of WRF during ADHF treatment was not associated with mortality. Our study suggests that development of WRF should not preclude diuresis in ADHF patients in the absence of volume depletion. PMID:22633437

Numerical Weather Prediction Models on Linux Boxes as tools in meteorological education in Hungary

NASA Astrophysics Data System (ADS)

Gyongyosi, A. Z.; Andre, K.; Salavec, P.; Horanyi, A.; Szepszo, G.; Mille, M.; Tasnadi, P.; Weidiger, T.

2012-04-01

Education of Meteorologist in Hungary - according to the Bologna Process - has three stages: BSc, MSc and PhD, and students graduating at each stage get the respective degree (BSc, MSc and PhD). The three year long base BSc course in Meteorology can be chosen by undergraduate students in the fields of Geosciences, Environmental Sciences and Physics. BasicsFundamentals in Mathematics (Calculus), Physics (General and Theoretical) Physics and Informatics are emphasized during their elementary education. The two year long MSc course - in which about 15 to 25 students are admitted each year - can be studied only at our the Eötvös Loránd uUniversity in the our country. Our aim is to give a basic education in all fields of Meteorology. Main topics are: Climatology, Atmospheric Physics, Atmospheric Chemistry, Dynamic and Synoptic Meteorology, Numerical Weather Prediction, modeling Modeling of surfaceSurface-atmosphere Iinteractions and Cclimate change. Education is performed in two branches: Climate Researcher and Forecaster. Education of Meteorologist in Hungary - according to the Bologna Process - has three stages: BSc, MSc and PhD, and students graduating at each stage get the respective degree. The three year long BSc course in Meteorology can be chosen by undergraduate students in the fields of Geosciences, Environmental Sciences and Physics. Fundamentals in Mathematics (Calculus), (General and Theoretical) Physics and Informatics are emphasized during their elementary education. The two year long MSc course - in which about 15 to 25 students are admitted each year - can be studied only at the Eötvös Loránd University in our country. Our aim is to give a basic education in all fields of Meteorology: Climatology, Atmospheric Physics, Atmospheric Chemistry, Dynamic and Synoptic Meteorology, Numerical Weather Prediction, Modeling of Surface-atmosphere Interactions and Climate change. Education is performed in two branches: Climate Researcher and Forecaster. Numerical modeling became a common tool in the daily practice of weather experts forecasters due to the i) increasing user demands for weather data by the costumers, ii) the growth in computer resources, iii) numerical weather prediction systems available for integration on affordable, off the shelf computers and iv) available input data (from ECMWF or NCEP) for model integrations. Beside learning the theoretical basis, since the last year. Students in their MSc or BSc Thesis Research or in Student's Research ProjectsStudent's Research Projects h have the opportunity to run numerical models and to analyze the outputs for different purposes including wind energy estimation, simulation of the dynamics of a polar low, and subtropical cyclones, analysis of the isentropic potential vorticity field, examination of coupled atmospheric dispersion models, etc. A special course in the application of numerical modeling has been held (is being announced for the upcoming semester) (is being announced for the upcoming semester) for our students in order to improve their skills on this field. Several numerical model (NRIPR ETA and WRF) systems have been adapted in the University and integrated WRF have been tested and used for the geographical region of the Carpathian Basin (NRIPR, ETA and WRF). Recently ALADIN/CHAPEAU the academic version of the ARPEGE ALADIN cy33t1 meso-scale numerical weather prediction model system (which is the operational forecasting tool of our National Weather Service) has been installed at our Institute. ALADIN is the operational forecasting model of the Hungarian Meteorological Service and developed in the framework of the international ALADIN co-operation. Our main objectives are i) the analysis of different typical weather situations, ii) fine tuning of parameterization schemes and the iii) comparison of the ALADIN/CHAPEAU and WRF model outputs based on case studies. The necessary hardware and software innovations has have been done. In the presentation the computer resources needed for the integration of both WRF and ALADIN/CHAPEAU models will be briefly described. The software developments performed for the evaluation and comparison of the different modeling systems will be demonstrated. The main objectives of the education program on the practical numerical weather modeling will be introduced, as well as its detailed thematics and the structure of the labs.

Validating the WRF-Chem model for wind energy applications using High Resolution Doppler Lidar data from a Utah 2012 field campaign

NASA Astrophysics Data System (ADS)

Mitchell, M. J.; Pichugina, Y. L.; Banta, R. M.

2015-12-01

Models are important tools for assessing potential of wind energy sites, but the accuracy of these projections has not been properly validated. In this study, High Resolution Doppler Lidar (HRDL) data obtained with high temporal and spatial resolution at heights of modern turbine rotors were compared to output from the WRF-chem model in order to help improve the performance of the model in producing accurate wind forecasts for the industry. HRDL data were collected from January 23-March 1, 2012 during the Uintah Basin Winter Ozone Study (UBWOS) field campaign. A model validation method was based on the qualitative comparison of the wind field images, time-series analysis and statistical analysis of the observed and modeled wind speed and direction, both for case studies and for the whole experiment. To compare the WRF-chem model output to the HRDL observations, the model heights and forecast times were interpolated to match the observed times and heights. Then, time-height cross-sections of the HRDL and WRF-Chem wind speed and directions were plotted to select case studies. Cross-sections of the differences between the observed and forecasted wind speed and directions were also plotted to visually analyze the model performance in different wind flow conditions. A statistical analysis includes the calculation of vertical profiles and time series of bias, correlation coefficient, root mean squared error, and coefficient of determination between two datasets. The results from this analysis reveals where and when the model typically struggles in forecasting winds at heights of modern turbine rotors so that in the future the model can be improved for the industry.

Customizing WRF-Hydro for the Laurentian Great Lakes Basin

NASA Astrophysics Data System (ADS)

Gronewold, A.; Pei, L.; Gochis, D.; Mason, L.; Sampson, K. M.; Dugger, A. L.; Read, L.; McCreight, J. L.; Xiao, C.; Lofgren, B. M.; Anderson, E. J.; Chu, P. Y.

2017-12-01

To advance the state of the art in regional hydrological forecasting, and to align with operational deployment of the National Water Model, a team of scientists has been customizing WRF-Hydro (the Weather Research and Forecasting model - Hydrological modeling extension package) to the entirety (including binational land and lake surfaces) of the Laurentian Great Lakes basin. Objectives of this customization project include opererational simulation and forecasting of the Great Lakes water balance and, in the short-term, research-oriented insights into modeling one- and two-way coupled lake-atmosphere and near-shore processes. Initial steps in this project have focused on overcoming inconsistencies in land surface hydrographic datasets between the United States and Canada. Improvements in the model's current representation of lake physics and stream routing are also critical components of this effort. Here, we present an update on the status of this project, including a synthesis of offline tests with WRF-Hydro based on the newly developed Great Lakes hydrographic data, and an assessment of the model's ability to simulate seasonal and multi-decadal hydrological response across the Great Lakes.

Applying a coupled hydrometeorological simulation system to flash flood forecasting over the Korean Peninsula

NASA Astrophysics Data System (ADS)

Ryu, Young; Lim, Yoon-Jin; Ji, Hee-Sook; Park, Hyun-Hee; Chang, Eun-Chul; Kim, Baek-Jo

2017-11-01

In flash flood forecasting, it is necessary to consider not only traditional meteorological variables such as precipitation, evapotranspiration, and soil moisture, but also hydrological components such as streamflow. To address this challenge, the application of high resolution coupled atmospheric-hydrological models is emerging as a promising alternative. This study demonstrates the feasibility of linking a coupled atmospheric-hydrological model (WRF/WRFHydro) with 150-m horizontal grid spacing for flash flood forecasting in Korea. The study area is the Namgang Dam basin in Southern Korea, a mountainous area located downstream of Jiri Mountain (1915 m in height). Under flash flood conditions, the simulated precipitation over the entire basin is comparable to the domain-averaged precipitation, but discharge data from WRF-Hydro shows some differences in the total available water and the temporal distribution of streamflow (given by the timing of the streamflow peak following precipitation), compared to observations. On the basis of sensitivity tests, the parameters controlling the infiltration of excess precipitation and channel roughness depending on stream order are refined and their influence on temporal distribution of streamflow is addressed with intent to apply WRF-Hydro to flash flood forecasting in the Namgang Dam basin. The simulation results from the WRF-Hydro model with optimized parameters demonstrate the potential utility of a coupled atmospheric-hydrological model for forecasting heavy rain-induced flash flooding over the Korean Peninsula.

Impact of parameterization of physical processes on simulation of track and intensity of tropical cyclone Nargis (2008) with WRF-NMM model.

PubMed

Pattanayak, Sujata; Mohanty, U C; Osuri, Krishna K

2012-01-01

The present study is carried out to investigate the performance of different cumulus convection, planetary boundary layer, land surface processes, and microphysics parameterization schemes in the simulation of a very severe cyclonic storm (VSCS) Nargis (2008), developed in the central Bay of Bengal on 27 April 2008. For this purpose, the nonhydrostatic mesoscale model (NMM) dynamic core of weather research and forecasting (WRF) system is used. Model-simulated track positions and intensity in terms of minimum central mean sea level pressure (MSLP), maximum surface wind (10 m), and precipitation are verified with observations as provided by the India Meteorological Department (IMD) and Tropical Rainfall Measurement Mission (TRMM). The estimated optimum combination is reinvestigated with six different initial conditions of the same case to have better conclusion on the performance of WRF-NMM. A few more diagnostic fields like vertical velocity, vorticity, and heat fluxes are also evaluated. The results indicate that cumulus convection play an important role in the movement of the cyclone, and PBL has a crucial role in the intensification of the storm. The combination of Simplified Arakawa Schubert (SAS) convection, Yonsei University (YSU) PBL, NMM land surface, and Ferrier microphysics parameterization schemes in WRF-NMM give better track and intensity forecast with minimum vector displacement error.

Evaluations of high-resolution dynamically downscaled ensembles over the contiguous United States

NASA Astrophysics Data System (ADS)

Zobel, Zachary; Wang, Jiali; Wuebbles, Donald J.; Kotamarthi, V. Rao

2018-02-01

This study uses Weather Research and Forecast (WRF) model to evaluate the performance of six dynamical downscaled decadal historical simulations with 12-km resolution for a large domain (7200 × 6180 km) that covers most of North America. The initial and boundary conditions are from three global climate models (GCMs) and one reanalysis data. The GCMs employed in this study are the Geophysical Fluid Dynamics Laboratory Earth System Model with Generalized Ocean Layer Dynamics component, Community Climate System Model, version 4, and the Hadley Centre Global Environment Model, version 2-Earth System. The reanalysis data is from the National Centers for Environmental Prediction-US. Department of Energy Reanalysis II. We analyze the effects of bias correcting, the lateral boundary conditions and the effects of spectral nudging. We evaluate the model performance for seven surface variables and four upper atmospheric variables based on their climatology and extremes for seven subregions across the United States. The results indicate that the simulation's performance depends on both location and the features/variable being tested. We find that the use of bias correction and/or nudging is beneficial in many situations, but employing these when running the RCM is not always an improvement when compared to the reference data. The use of an ensemble mean and median leads to a better performance in measuring the climatology, while it is significantly biased for the extremes, showing much larger differences than individual GCM driven model simulations from the reference data. This study provides a comprehensive evaluation of these historical model runs in order to make informed decisions when making future projections.

Convective and microphysics parameterization impact on simulating heavy rainfall in Semarang (case study on February 12th, 2015)

NASA Astrophysics Data System (ADS)

Faridatussafura, Nurzaka; Wandala, Agie

2018-05-01

The meteorological model WRF-ARW version 3.8.1 is used for simulating the heavy rainfall in Semarang that occurred on February 12th, 2015. Two different convective schemes and two different microphysics scheme in a nested configuration were chosen. The sensitivity of those schemes in capturing the extreme weather event has been tested. GFS data were used for the initial and boundary condition. Verification on the twenty-four hours accumulated rainfall using GSMaPsatellite data shows that Kain-Fritsch convective scheme and Lin microphysics scheme is the best combination scheme among the others. The combination also gives the highest success ratio value in placing high intensity rainfall area. Based on the ROC diagram, KF-Lin shows the best performance in detecting high intensity rainfall. However, the combination still has high bias value.

Assessment of the Effects of Various Precipitation Forcings on Flood Forecasting Potential Using WRF-Hydro Modeling

NASA Astrophysics Data System (ADS)

Zhang, J.; Fang, N. Z.

2017-12-01

A potential flood forecast system is under development for the Upper Trinity River Basin (UTRB) in North Central of Texas using the WRF-Hydro model. The Routing Application for the Parallel Computation of Discharge (RAPID) is utilized as channel routing module to simulate streamflow. Model performance analysis was conducted based on three quantitative precipitation estimates (QPE): the North Land Data Assimilation System (NLDAS) rainfall, the Multi-Radar Multi-Sensor (MRMS) QPE and the National Centers for Environmental Prediction (NCEP) quality-controlled stage IV estimates. Prior to hydrologic simulation, QPE performance is assessed on two time scales (daily and hourly) using the Community Collaborative Rain, Hail and Snow Network (CoCoRaHS) and Hydrometeorological Automated Data System (HADS) hourly products. The calibrated WRF-Hydro model was then evaluated by comparing the simulated against the USGS observed using various QPE products. The results imply that the NCEP stage IV estimates have the best accuracy among the three QPEs on both time scales, while the NLDAS rainfall performs poorly because of its coarse spatial resolution. Furthermore, precipitation bias demonstrates pronounced impact on flood forecasting skills, as the root mean squared errors are significantly reduced by replacing NLDAS rainfall with NCEP stage IV estimates. This study also demonstrates that accurate simulated results can be achieved when initial soil moisture values are well understood in the WRF-Hydro model. Future research effort will therefore be invested on incorporating data assimilation with focus on initial states of the soil properties for UTRB.

Short-term Wind Forecasting at Wind Farms using WRF-LES and Actuator Disk Model

NASA Astrophysics Data System (ADS)

Kirkil, Gokhan

2017-04-01

Short-term wind forecasts are obtained for a wind farm on a mountainous terrain using WRF-LES. Multi-scale simulations are also performed using different PBL parameterizations. Turbines are parameterized using Actuator Disc Model. LES models improved the forecasts. Statistical error analysis is performed and ramp events are analyzed. Complex topography of the study area affects model performance, especially the accuracy of wind forecasts were poor for cross valley-mountain flows. By means of LES, we gain new knowledge about the sources of spatial and temporal variability of wind fluctuations such as the configuration of wind turbines.

Regional Climate Modeling over the Marmara Region, Turkey, with Improved Land Cover Data

NASA Astrophysics Data System (ADS)

Sertel, E.; Robock, A.

2007-12-01

Land surface controls the partitioning of available energy at the surface between sensible and latent heat,and controls partitioning of available water between evaporation and runoff. Current land cover data available within the regional climate models such as Regional Atmospheric Modeling System (RAMS), the Fifth-Generation NCAR/Penn State Mesoscale Model (MM5) and Weather Research and Forecasting (WRF) was obtained from 1- km Advanced Very High Resolution Radiometer satellite images spanning April 1992 through March 1993 with an unsupervised classification technique. These data are not up-to-date and are not accurate for all regions and some land cover types such as urban areas. Here we introduce new, up-to-date and accurate land cover data for the Marmara Region, Turkey derived from Landsat Enhanced Thematic Mapper images into the WRF regional climate model. We used several image processing techniques to create accurate land cover data from Landsat images obtained between 2001 and 2005. First, all images were atmospherically and radiometrically corrected to minimize contamination effects of atmospheric particles and systematic errors. Then, geometric correction was performed for each image to eliminate geometric distortions and define images in a common coordinate system. Finally, unsupervised and supervised classification techniques were utilized to form the most accurate land cover data yet for the study area. Accuracy assessments of the classifications were performed using error matrix and kappa statistics to find the best classification results. Maximum likelihood classification method gave the most accurate results over the study area. We compared the new land cover data with the default WRF land cover data. WRF land cover data cannot represent urban areas in the cities of Istanbul, Izmit, and Bursa. As an example, both original satellite images and new land cover data showed the expansion of urban areas into the Istanbul metropolitan area, but in the WRF land cover data only a limited area along the Bosporus is shown as urban. In addition, the new land cover data indicate that the northern part of Istanbul is covered by evergreen and deciduous forest (verified by ground truth data), but the WRF data indicate that most of this region is croplands. In the northern part of the Marmara Region, there is bare ground as a result of open mining activities and this class can be identified in our land cover data, whereas the WRF data indicated this region as woodland. We then used this new data set to conduct WRF simulations for one main and two nested domains, where the inner-most domain represents the Marmara Region with 3 km horizontal resolution. The vertical domain of both main and nested domains extends over 28 vertical levels. Initial and boundary conditions were obtained from National Centers for Environmental Prediction-Department of Energy Reanalysis II and the Noah model was selected as the land surface model. Two model simulations were conducted; one with available land cover data and one with the newly created land cover data. Using detailed meteorological station data within the study area, we find that the simulation with the new land cover data set produces better temperature and precipitation simulations for the region, showing the value of accurate land cover data and that changing land cover data can be an important influence on local climate change.

Meteorological overview and plume transport patterns during Cal-Mex 2010

NASA Astrophysics Data System (ADS)

Bei, Naifang; Li, Guohui; Zavala, Miguel; Barrera, Hugo; Torres, Ricardo; Grutter, Michel; Gutiérrez, Wilfredo; García, Manuel; Ruiz-Suarez, Luis Gerardo; Ortinez, Abraham; Guitierrez, Yaneth; Alvarado, Carlos; Flores, Israel; Molina, Luisa T.

2013-05-01

Cal-Mex 2010 Field Study is a US-Mexico collaborative project to investigate cross-border transport of emissions in the California-Mexico border region, which took place from May 15 to June 30, 2010. The current study presents an overview of the meteorological conditions and plume transport patterns during Cal-Mex 2010 based on the analysis of surface and vertical measurements (radiosonde, ceilometers and tethered balloon) conducted in Tijuana, Mexico and the modeling output using a trajectory model (FLEXPRT-WRF) and a regional model (WRF). The WRF model has been applied for providing the meteorological daily forecasts that are verified using the available observations. Both synoptic-scale and urban-scale forecasts (including wind, temperature, and humidity) agree reasonably well with the NCEP-FNL reanalysis data and the measurements; however, the WRF model frequently underestimates surface temperature and planetary boundary layer (PBL) height during nighttime compared to measurements. Based on the WRF-FLEXPART simulations with particles released in Tijuana in the morning, four representative plume transport patterns are identified as “plume-southeast”, “plume-southwest”, “plume-east” and “plume-north”, indicating the downwind direction of the plume; this will be useful for linking meteorological conditions with observed changes in trace gases and particular matter (PM). Most of the days during May and June are classified as plume-east and plume-southeast days, showing that the plumes in Tijuana are mostly carried to the southeast and east of Tijuana within the boundary layer during daytime. The plume transport directions are generally consistent with the prevailing wind directions on 850 hPa. The low level (below 800 m) wind, temperature, and moisture characteristics are different for each plume transport category according to the measurements from the tethered balloon. Future studies (such as using data assimilation and ensemble forecasts) will be performed to improve the temperature, wind and PBL simulations.

Methane in the Amazon: A forward and inverse regional modeling approach

NASA Astrophysics Data System (ADS)

Beck, V.; Gerbig, C.; Koch, F. T.; Karstens, U.; Chen, H.; Bela, M. M.; Longo, K.; Freitas, S.; Bergamaschi, P. M.; Kaplan, J. O.; Prigent, C.

2011-12-01

The Amazon region is an important player in the global methane (CH4) cycle, the second most important greenhouse gas after CO2. Different major CH4 sources in the Amazon region such as anaerobic microbial production in wetlands and biomass burning will be affected by changing climate. Therefore, a thorough understanding of the processes is required. Within the BARCA (Balanço Atmosférico Regional de Carbono na Amazônia) project, airborne measurements of greenhouse gases, associated tracers and aerosols were taken during the end of the dry season in November 2008 as well as during the end of the wet season in May 2009. These aircraft measurements and additional ground based measurements provide a test bed for high resolution transport simulation of CH4. Here we present a comparison of WRF-Chem passive tracer simulations of CH4 to airborne CH4 observations obtained from the BARCA campaigns in November 2008 and May 2009 using the newly established WRF Greenhouse Gas Model (WRF-GHG) in combination with two different process-based bottom-up models for the calculation of CH4 emissions from anaerobic microbial production in wetlands (Kaplan and Walter-Heimann) and three different wetland inundation maps (Kaplan, JERS-1SAR, Prigent). The comparison illustrates the importance of a wetland inundation map with inundated area changing in time, and the quality of the representation of atmospheric transport in regional models in tropical regions. In addition, we demonstrate a comparison of WRF-GHG CH4 simulations to TT34 tower observations (35 m above ground; located 60 km north-west of Manaus, Brazil) for August 2009, evaluating the performance of WRF-GHG in representing CH4 observations in the planetary boundary layer in tropical regions. Finally, we present preliminary results of a regional inversion using the TM3-STILT model together with the above mentioned observations for the estimation of the CH4 budget of the Amazon region.

Transient and persistent worsening renal function during hospitalization for acute heart failure.

PubMed

Krishnamoorthy, Arun; Greiner, Melissa A; Sharma, Puza P; DeVore, Adam D; Johnson, Katherine Waltman; Fonarow, Gregg C; Curtis, Lesley H; Hernandez, Adrian F

2014-12-01

Transient and persistent worsening renal function (WRF) may be associated with different risks during hospitalization for acute heart failure. We compared outcomes of patients hospitalized for acute heart failure with transient, persistent, or no WRF. We identified patients 65 years or older hospitalized with acute heart failure from a clinical registry linked to Medicare claims data. We defined WRF as an increase in serum creatinine of ≥ 0.3 mg/dL after admission. We further classified patients with WRF by the difference between admission and last recorded serum creatinine levels into transient WRF (< 0.3 mg/dL) or persistent WRF (≥ 0.3 mg/dL). We examined unadjusted rates and adjusted associations between 90-day outcomes and WRF status. Among 27,309 patients, 18,568 (68.0%) had no WRF, 3,205 (11.7%) had transient WRF, and 5,536 (20.3%) had persistent WRF. Patients with WRF had higher observed rates of 90-day postdischarge all-cause readmission and 90-day postadmission mortality (P < .001). After multivariable adjustment, transient WRF (hazard ratio [HR] 1.19, 99% CI 1.05-1.35) and persistent WRF (HR 1.73, 99% CI 1.57-1.91) were associated with higher risks of 90-day postadmission mortality (P < .001 for both). Compared with transient WRF, persistent WRF was associated with a higher risk of 90-day postadmission mortality (HR 1.46, 99% CI 1.28-1.66, P < .001). Transient and persistent WRF during hospitalization for acute heart failure were associated with higher adjusted risks for 90-day all-cause postadmission mortality. Patients with persistent WRF had worse outcomes. Copyright © 2014 Elsevier Inc. All rights reserved.

Related factors for worsening renal function following percutaneous transluminal renal angioplasty (PTRA) in patients with atherosclerotic renal artery stenosis.

PubMed

Yoshihara, Fumiki; Fukuda, Tetsuya; Iwashima, Yoshio; Nakamura, Satoko; Hayashi, Shin-Ichiro; Kishida, Masatsugu; Ishizuka, Azusa; Kusunoki, Hiroshi; Ohta, Yuko; Kawano, Yuhei

2015-01-01

To identify candidates for PTRA in terms of the preservation of renal function, we herein evaluated factors that caused worsening renal function (WRF) after PTRA. We evaluated 92 patients with atherosclerotic renal artery stenosis (mean age 70.7 ± 8.4 years). WRF was defined as a ≥0.3 mg/dL increase in creatinine levels after PTRA compared to before PTRA. A total of 92 patients exhibited non-WRF 83 (90.2%), WRF 9 (9.8%). Significant differences were observed in serum creatinine levels between two groups both before (non-WRF 1.34 ± 0.49 versus WRF 1.70 ± 0.68 mg/dL, p = 0.0462) and after PTRA (non-WRF 1.31 ± 0.43 versus WRF 2.42 ± 1.12 mg/dL, p < 0.0001). Patients with WRF had higher comorbidity rate of diabetes mellitus (DM) (non-WRF 31.3% versus WRF 66.7%, p = 0.0345) and proteinuria (non-WRF 27.7% versus WRF 66.7%, p = 0.0169), and had higher systolic blood pressure (non-WRF 143.6 ± 18.7 versus WRF 157.1 ± 19.9 mmHg, p = 0.0436), higher plasma B-type natriuretic peptide (BNP) levels, and larger left atrial and left ventricular end-diastolic dimensions before PTRA. Patients with WRF had a higher rate of taking diuretics (non-WRF 27.7% versus WRF 66.7%, p = 0.0169) after PTRA. Multiple logistic regression analysis revealed that comorbidity of DM was an independent related factor for WRF (comorbidity of DM, yes: OR 31.0, 95% CI 2.44-1024.62, p = 0.0055). Comorbidity of DM, coexisting of proteinuria, high creatinine level, high blood pressure, high BNP levels, and large left atrial and ventricular dimensions were related to WRF after PTRA in patients with atherosclerotic renal artery stenosis.

Chronic kidney disease and worsening renal function in acute heart failure: different phenotypes with similar prognostic impact?

PubMed

Palazzuoli, Alberto; Lombardi, Carlo; Ruocco, Gaetano; Padeletti, Margherita; Nuti, Ranuccio; Metra, Marco; Ronco, Claudio

2016-12-01

Nearly a third of patients with acute heart failure experience concomitant renal dysfunction. This condition is often associated with increased costs of care, length of hospitalisation and high mortality. Although the clinical impact of chronic kidney disease (CKD) has been well established, the exact clinical significance of worsening renal function (WRF) during the acute and post-hospitalisation phases is not completely understood. Therefore, it is still unclear which of the common laboratory markers are able to identify WRF at an early stage. Recent studies comparing CKD with WRF showed contradictory results; this could depend on a different WRF definition, clinical characteristics, haemodynamic disorders and the presence of prior renal dysfunction in the population enrolled. The current definition of acute cardiorenal syndrome focuses on both the heart and kidney but it lacks precise laboratory marker cut-offs and a specific diagnostic approach. WRF and CKD could represent different pathophysiological mechanisms in the setting of acute heart failure; the traditional view includes reduced cardiac output with systemic and renal vasoconstriction. Nevertheless, it has become a mixed model that encompasses both forward and backward haemodynamic dysfunction. Increased central venous pressure, renal congestion with tubular obliteration, tubulo-glomerular feedback and increased abdominal pressure are all potential additional contributors. The impact of WRF on patients who experience preserved renal function and individuals affected with CKD is currently unknown. Therefore it is extremely important to understand the origins, the clinical significance and the prognostic impact of WRF on CKD. © The European Society of Cardiology 2015.

A meteo-hydrological modelling system for the reconstruction of river runoff: the case of the Ofanto river catchment

NASA Astrophysics Data System (ADS)

Verri, Giorgia; Pinardi, Nadia; Gochis, David; Tribbia, Joseph; Navarra, Antonio; Coppini, Giovanni; Vukicevic, Tomislava

2017-10-01

A meteo-hydrological modelling system has been designed for the reconstruction of long time series of rainfall and river runoff events. The modelling chain consists of the mesoscale meteorological model of the Weather Research and Forecasting (WRF), the land surface model NOAH-MP and the hydrology-hydraulics model WRF-Hydro. Two 3-month periods are reconstructed for winter 2011 and autumn 2013, containing heavy rainfall and river flooding events. Several sensitivity tests were performed along with an assessment of which tunable parameters, numerical choices and forcing data most impacted on the modelling performance.The calibration of the experiments highlighted that the infiltration and aquifer coefficients should be considered as seasonally dependent.The WRF precipitation was validated by a comparison with rain gauges in the Ofanto basin. The WRF model was demonstrated to be sensitive to the initialization time and a spin-up of about 1.5 days was needed before the start of the major rainfall events in order to improve the accuracy of the reconstruction. However, this was not sufficient and an optimal interpolation method was developed to correct the precipitation simulation. It is based on an objective analysis (OA) and a least square (LS) melding scheme, collectively named OA+LS. We demonstrated that the OA+LS method is a powerful tool to reduce the precipitation uncertainties and produce a lower error precipitation reconstruction that itself generates a better river discharge time series. The validation of the river streamflow showed promising statistical indices.The final set-up of our meteo-hydrological modelling system was able to realistically reconstruct the local rainfall and the Ofanto hydrograph.

Analysis of errors introduced by geographic coordinate systems on weather numeric prediction modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cao, Yanni; Cervone, Guido; Barkley, Zachary

Most atmospheric models, including the Weather Research and Forecasting (WRF) model, use a spherical geographic coordinate system to internally represent input data and perform computations. However, most geographic information system (GIS) input data used by the models are based on a spheroid datum because it better represents the actual geometry of the earth. WRF and other atmospheric models use these GIS input layers as if they were in a spherical coordinate system without accounting for the difference in datum. When GIS layers are not properly reprojected, latitudinal errors of up to 21 km in the midlatitudes are introduced. Recent studiesmore » have suggested that for very high-resolution applications, the difference in datum in the GIS input data (e.g., terrain land use, orography) should be taken into account. However, the magnitude of errors introduced by the difference in coordinate systems remains unclear. This research quantifies the effect of using a spherical vs. a spheroid datum for the input GIS layers used by WRF to study greenhouse gas transport and dispersion in northeast Pennsylvania.« less

Analysis of errors introduced by geographic coordinate systems on weather numeric prediction modeling

DOE PAGES

Cao, Yanni; Cervone, Guido; Barkley, Zachary; ...

2017-09-19

Most atmospheric models, including the Weather Research and Forecasting (WRF) model, use a spherical geographic coordinate system to internally represent input data and perform computations. However, most geographic information system (GIS) input data used by the models are based on a spheroid datum because it better represents the actual geometry of the earth. WRF and other atmospheric models use these GIS input layers as if they were in a spherical coordinate system without accounting for the difference in datum. When GIS layers are not properly reprojected, latitudinal errors of up to 21 km in the midlatitudes are introduced. Recent studiesmore » have suggested that for very high-resolution applications, the difference in datum in the GIS input data (e.g., terrain land use, orography) should be taken into account. However, the magnitude of errors introduced by the difference in coordinate systems remains unclear. This research quantifies the effect of using a spherical vs. a spheroid datum for the input GIS layers used by WRF to study greenhouse gas transport and dispersion in northeast Pennsylvania.« less

Simulated cold bias being improved by using MODIS time-varying albedo in the Tibetan Plateau in WRF model

NASA Astrophysics Data System (ADS)

Meng, X.; Lyu, S.; Zhang, T.; Zhao, L.; Li, Z.; Han, B.; Li, S.; Ma, D.; Chen, H.; Ao, Y.; Luo, S.; Shen, Y.; Guo, J.; Wen, L.

2018-04-01

Systematic cold biases exist in the simulation for 2 m air temperature in the Tibetan Plateau (TP) when using regional climate models and global atmospheric general circulation models. We updated the albedo in the Weather Research and Forecasting (WRF) Model lower boundary condition using the Global LAnd Surface Satellite Moderate-Resolution Imaging Spectroradiometer albedo products and demonstrated evident improvement for cold temperature biases in the TP. It is the large overestimation of albedo in winter and spring in the WRF model that resulted in the large cold temperature biases. The overestimated albedo was caused by the simulated precipitation biases and over-parameterization of snow albedo. Furthermore, light-absorbing aerosols can result in a large reduction of albedo in snow and ice cover. The results suggest the necessity of developing snow albedo parameterization using observations in the TP, where snow cover and melting are very different from other low-elevation regions, and the influence of aerosols should be considered as well. In addition to defining snow albedo, our results show an urgent call for improving precipitation simulation in the TP.

A comparison of river discharge calculated by using a regional climate model output with different reanalysis datasets in 1980s and 1990s

NASA Astrophysics Data System (ADS)

Ma, X.; Yoshikane, T.; Hara, M.; Adachi, S. A.; Wakazuki, Y.; Kawase, H.; Kimura, F.

2014-12-01

To check the influence of boundary input data on a modeling result, we had a numerical investigation of river discharge by using runoff data derived by a regional climate model with a 4.5-km resolution as input data to a hydrological model. A hindcast experiment, which to reproduce the current climate was carried out for the two decades, 1980s and 1990s. We used the Advanced Research WRF (ARW) (ver. 3.2.1) with a two-way nesting technique and the WRF single-moment 6-class microphysics scheme. Noah-LSM is adopted to simulate the land surface process. The NCEP/NCAR and ERA-Interim 6-hourly reanalysis datasets were used as the lateral boundary condition for the runs, respectively. The output variables used for river discharge simulation from the WRF model were underground runoff and surface runoff. Four rivers (Mogami, Agano, Jinzu and Tone) were selected in this study. The results showed that the characteristic of river discharge in seasonal variation could be represented and there were overestimated compared with measured one.

Analysis of errors introduced by geographic coordinate systems on weather numeric prediction modeling

NASA Astrophysics Data System (ADS)

Cao, Yanni; Cervone, Guido; Barkley, Zachary; Lauvaux, Thomas; Deng, Aijun; Taylor, Alan

2017-09-01

Most atmospheric models, including the Weather Research and Forecasting (WRF) model, use a spherical geographic coordinate system to internally represent input data and perform computations. However, most geographic information system (GIS) input data used by the models are based on a spheroid datum because it better represents the actual geometry of the earth. WRF and other atmospheric models use these GIS input layers as if they were in a spherical coordinate system without accounting for the difference in datum. When GIS layers are not properly reprojected, latitudinal errors of up to 21 km in the midlatitudes are introduced. Recent studies have suggested that for very high-resolution applications, the difference in datum in the GIS input data (e.g., terrain land use, orography) should be taken into account. However, the magnitude of errors introduced by the difference in coordinate systems remains unclear. This research quantifies the effect of using a spherical vs. a spheroid datum for the input GIS layers used by WRF to study greenhouse gas transport and dispersion in northeast Pennsylvania.

The role of evapotranspiration fluxes in summertime precipitation in Central Europe: coupled groundwater-atmosphere simulations with the WRF-LEAFHYDRO system.

NASA Astrophysics Data System (ADS)

Regueiro Sanfiz, Sabela; Gómez, Breo; Miguez Macho, Gonzalo

2017-04-01

Because of its continental position, Central Europe summertime rainfall is largely dependent on local or regional dynamics, with precipitation water possibly also significantly dependent on local sources. We investigate here land-atmosphere feedbacks over inland Europe focusing in particular on evapotranspiration-soil moisture connections and precipitation recycling ratios. For this purpose, a set of simulations were performed with the Weather Research and Forecasting (WRF) model coupled to LEAFHYDRO soil-vegetation-hydrology model. The LEAFHYDRO Land Surface Model includes a groundwater parameterization with a dynamic water table fully coupling groundwater to the soil-vegetation and surface waters via two-way fluxes. A water tagging capability in the WRF model is used to quantify evapotranspiration contribution to precipitation over the region. Several years are considered, including summertime 2002, during which severe flooding occurred. Preliminary results from our simulations highlight the link of large areas with shallow water with high air moisture values through the summer season; and the importance of the contribution of evapotranspiration to summertime precipitation. Consequently, results show the advantages of using a fully coupled hydrology-atmospheric modeling system.

Toward Improved Land Surface Initialization in Support of Regional WRF Forecasts at the Kenya Meteorological Department

NASA Technical Reports Server (NTRS)

Case. Jonathan; Mungai, John; Sakwa, Vincent; Kabuchanga, Eric; Zavodsky, Bradley T.; Limaye, Ashutosh S.

2014-01-01

Flooding and drought are two key forecasting challenges for the Kenya Meteorological Department (KMD). Atmospheric processes leading to excessive precipitation and/or prolonged drought can be quite sensitive to the state of the land surface, which interacts with the boundary layer of the atmosphere providing a source of heat and moisture. The development and evolution of precipitation systems are affected by heat and moisture fluxes from the land surface within weakly-sheared environments, such as in the tropics and sub-tropics. These heat and moisture fluxes during the day can be strongly influenced by land cover, vegetation, and soil moisture content. Therefore, it is important to represent the land surface state as accurately as possible in numerical weather prediction models. Enhanced regional modeling capabilities have the potential to improve forecast guidance in support of daily operations and high-end events over east Africa. KMD currently runs a configuration of the Weather Research and Forecasting (WRF) model in real time to support its daily forecasting operations, invoking the Nonhydrostatic Mesoscale Model (NMM) dynamical core. They make use of the National Oceanic and Atmospheric Administration / National Weather Service Science and Training Resource Center's Environmental Modeling System (EMS) to manage and produce the WRF-NMM model runs on a 7-km regional grid over eastern Africa. Two organizations at the National Aeronautics and Space Administration Marshall Space Flight Center in Huntsville, AL, SERVIR and the Short-term Prediction Research and Transition (SPoRT) Center, have established a working partnership with KMD for enhancing its regional modeling capabilities. To accomplish this goal, SPoRT and SERVIR will provide experimental land surface initialization datasets and model verification capabilities to KMD. To produce a land-surface initialization more consistent with the resolution of the KMD-WRF runs, the NASA Land Information System (LIS) will be run at a comparable resolution to provide real-time, daily soil initialization data in place of interpolated Global Forecast System soil moisture and temperature data. Additionally, real-time green vegetation fraction data from the Visible Infrared Imaging Radiometer Suite will be incorporated into the KMD-WRF runs, once it becomes publicly available from the National Environmental Satellite Data and Information Service. Finally, model verification capabilities will be transitioned to KMD using the Model Evaluation Tools (MET) package, in order to quantify possible improvements in simulated temperature, moisture and precipitation resulting from the experimental land surface initialization. The transition of these MET tools will enable KMD to monitor model forecast accuracy in near real time. This presentation will highlight preliminary verification results of WRF runs over east Africa using the LIS land surface initialization.

Automated system for smoke dispersion prediction due to wild fires in Alaska

NASA Astrophysics Data System (ADS)

Kulchitsky, A.; Stuefer, M.; Higbie, L.; Newby, G.

2007-12-01

Community climate models have enabled development of specific environmental forecast systems. The University of Alaska (UAF) smoke group was created to adapt a smoke forecast system to the Alaska region. The US Forest Service (USFS) Missoula Fire Science Lab had developed a smoke forecast system based on the Weather Research and Forecasting (WRF) Model including chemistry (WRF/Chem). Following the successful experience of USFS, which runs their model operationally for the contiguous U.S., we develop a similar system for Alaska in collaboration with scientists from the USFS Missoula Fire Science Lab. Wildfires are a significant source of air pollution in Alaska because the climate and vegetation favor annual summer fires that burn huge areas. Extreme cases occurred in 2004, when an area larger than Maryland (more than 25000~km2) burned. Small smoke particles with a diameter less than 10~μm can penetrate deep into lungs causing health problems. Smoke also creates a severe restriction to air transport and has tremendous economical effect. The smoke dispersion and forecast system for Alaska was developed at the Geophysical Institute (GI) and the Arctic Region Supercomputing Center (ARSC), both at University of Alaska Fairbanks (UAF). They will help the public and plan activities a few days in advance to avoid dangerous smoke exposure. The availability of modern high performance supercomputers at ARSC allows us to create and run high-resolution, WRF-based smoke dispersion forecast for the entire State of Alaska. The core of the system is a Python program that manages the independent pieces. Our adapted Alaska system performs the following steps \\begin{itemize} Calculate the medium-resolution weather forecast using WRF/Met. Adapt the near real-time satellite-derived wildfire location and extent data that are received via direct broadcast from UAF's "Geographic Information Network of Alaska" (GINA) Calculate fuel moisture using WRF forecasts and National Fire Danger Rating System (NFDRS) fuel maps Calculate smoke emission components using a first order fire emission model Model the smoke plume rise yielding a vertically distribution that accounts for one-dimensional (vertical) concentrations of smoke constituents in the atmosphere above the fire Run WRF/Chem at high resolution for the forecast Use standard graphical tools to provide accessible smoke dispersion The system run twice each day at ARSC. The results will be freely available from a dedicated wildfire smoke web portal at ARSC.

A High-Resolution WRF Tropical Channel Simulation Driven by a Global Reanalysis

NASA Astrophysics Data System (ADS)

Holland, G.; Leung, L.; Kuo, Y.; Hurrell, J.

2006-12-01

Since 2003, NCAR has invested in the development and application of Nested Regional Climate Model (NRCM) based on the Weather Research and Forecasting (WRF) model and the Community Climate System Model, as a key component of the Prediction Across Scales Initiative. A prototype tropical channel model has been developed to investigate scale interactions and the influence of tropical convection on large scale circulation and tropical modes. The model was developed based on the NCAR Weather Research and Forecasting Model (WRF), configured as a tropical channel between 30 ° S and 45 ° N, wide enough to allow teleconnection effects over the mid-latitudes. Compared to the limited area domain that WRF is typically applied over, the channel mode alleviates issues with reflection of tropical modes that could result from imposing east/west boundaries. Using a large amount of available computing resources on a supercomputer (Blue Vista) during its bedding in period, a simulation has been completed with the tropical channel applied at 36 km horizontal resolution for 5 years from 1996 to 2000, with large scale circulation provided by the NCEP/NCAR global reanalysis at the north/south boundaries. Shorter simulations of 2 years and 6 months have also been performed to include two-way nests at 12 km and 4 km resolution, respectively, over the western Pacific warm pool, to explicitly resolve tropical convection in the Maritime Continent. The simulations realistically captured the large-scale circulation including the trade winds over the tropical Pacific and Atlantic, the Australian and Asian monsoon circulation, and hurricane statistics. Preliminary analysis and evaluation of the simulations will be presented.

Nesting large-eddy simulations within mesoscale simulations for wind energy applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lundquist, J K; Mirocha, J D; Chow, F K

2008-09-08

With increasing demand for more accurate atmospheric simulations for wind turbine micrositing, for operational wind power forecasting, and for more reliable turbine design, simulations of atmospheric flow with resolution of tens of meters or higher are required. These time-dependent large-eddy simulations (LES), which resolve individual atmospheric eddies on length scales smaller than turbine blades and account for complex terrain, are possible with a range of commercial and open-source software, including the Weather Research and Forecasting (WRF) model. In addition to 'local' sources of turbulence within an LES domain, changing weather conditions outside the domain can also affect flow, suggesting thatmore » a mesoscale model provide boundary conditions to the large-eddy simulations. Nesting a large-eddy simulation within a mesoscale model requires nuanced representations of turbulence. Our group has improved the Weather and Research Forecasting model's (WRF) LES capability by implementing the Nonlinear Backscatter and Anisotropy (NBA) subfilter stress model following Kosovic (1997) and an explicit filtering and reconstruction technique to compute the Resolvable Subfilter-Scale (RSFS) stresses (following Chow et al, 2005). We have also implemented an immersed boundary method (IBM) in WRF to accommodate complex terrain. These new models improve WRF's LES capabilities over complex terrain and in stable atmospheric conditions. We demonstrate approaches to nesting LES within a mesoscale simulation for farms of wind turbines in hilly regions. Results are sensitive to the nesting method, indicating that care must be taken to provide appropriate boundary conditions, and to allow adequate spin-up of turbulence in the LES domain.« less

Development and verification of a new wind speed forecasting system using an ensemble Kalman filter data assimilation technique in a fully coupled hydrologic and atmospheric model

NASA Astrophysics Data System (ADS)

Williams, John L.; Maxwell, Reed M.; Monache, Luca Delle

2013-12-01

Wind power is rapidly gaining prominence as a major source of renewable energy. Harnessing this promising energy source is challenging because of the chaotic nature of wind and its inherently intermittent nature. Accurate forecasting tools are critical to support the integration of wind energy into power grids and to maximize its impact on renewable energy portfolios. We have adapted the Data Assimilation Research Testbed (DART), a community software facility which includes the ensemble Kalman filter (EnKF) algorithm, to expand our capability to use observational data to improve forecasts produced with a fully coupled hydrologic and atmospheric modeling system, the ParFlow (PF) hydrologic model and the Weather Research and Forecasting (WRF) mesoscale atmospheric model, coupled via mass and energy fluxes across the land surface, and resulting in the PF.WRF model. Numerous studies have shown that soil moisture distribution and land surface vegetative processes profoundly influence atmospheric boundary layer development and weather processes on local and regional scales. We have used the PF.WRF model to explore the connections between the land surface and the atmosphere in terms of land surface energy flux partitioning and coupled variable fields including hydraulic conductivity, soil moisture, and wind speed and demonstrated that reductions in uncertainty in these coupled fields realized through assimilation of soil moisture observations propagate through the hydrologic and atmospheric system. The sensitivities found in this study will enable further studies to optimize observation strategies to maximize the utility of the PF.WRF-DART forecasting system.

Decadal application of WRF/chem for regional air quality and climate modeling over the U.S. under the representative concentration pathways scenarios. Part 2: Current vs. future simulations

NASA Astrophysics Data System (ADS)

Yahya, Khairunnisa; Campbell, Patrick; Zhang, Yang

2017-03-01

Following a comprehensive model evaluation, this Part II paper presents projected changes in future (2046-2055) climate, air quality, and their interactions under the RCP4.5 and RCP8.5 scenarios using the Weather, Research and Forecasting model with Chemistry (WRF/Chem). In general, both WRF/Chem RCP4.5 and RCP8.5 simulations predict similar increases on average (∼2 °C) for 2-m temperature (T2) but different spatial distributions of the projected changes in T2, 2-m relative humidity, 10-m wind speed, precipitation, and planetary boundary layer height, due to differences in the spatial distributions of projected emissions, and their feedbacks into climate. Future O3 mixing ratios will decrease for most parts of the U.S. under the RCP4.5 scenario but increase for all areas under the RCP8.5 scenario due to higher projected temperature, greenhouse gas concentrations and biogenic volatile organic compounds (VOC) emissions, higher O3 values for boundary conditions, and disbenefit of NOx reduction and decreased NO titration over VOC-limited O3 chemistry regions. Future PM2.5 concentrations will decrease for both RCP4.5 and RCP8.5 scenarios with different trends in projected concentrations of individual PM species. Total cloud amounts decrease under both scenarios in the future due to decreases in PM and cloud droplet number concentration thus increased radiation. Those results illustrate the impacts of carbon policies with different degrees of emission reductions on future climate and air quality. The WRF/Chem and WRF simulations show different spatial patterns for projected changes in T2 for future decade, indicating different impacts of prognostic and prescribed gas/aerosol concentrations, respectively, on climate change.

High-Resolution WRF Forecasts of Lightning Threat

NASA Technical Reports Server (NTRS)

Goodman, S. J.; McCaul, E. W., Jr.; LaCasse, K.

2007-01-01

Tropical Rainfall Measuring Mission (TRMM)lightning and precipitation observations have confirmed the existence of a robust relationship between lightning flash rates and the amount of large precipitating ice hydrometeors in storms. This relationship is exploited, in conjunction with the capabilities of the Weather Research and Forecast (WRF) model, to forecast the threat of lightning from convective storms using the output fields from the model forecasts. The simulated vertical flux of graupel at -15C is used in this study as a proxy for charge separation processes and their associated lightning risk. Initial experiments using 6-h simulations are conducted for a number of case studies for which three-dimensional lightning validation data from the North Alabama Lightning Mapping Array are available. The WRF has been initialized on a 2 km grid using Eta boundary conditions, Doppler radar radial velocity and reflectivity fields, and METAR and ACARS data. An array of subjective and objective statistical metrics is employed to document the utility of the WRF forecasts. The simulation results are also compared to other more traditional means of forecasting convective storms, such as those based on inspection of the convective available potential energy field.

Assessing the Performance of Computationally Simple and Complex Representations of Aerosol Processes using a Testbed Methodology

NASA Astrophysics Data System (ADS)

Fast, J. D.; Ma, P.; Easter, R. C.; Liu, X.; Zaveri, R. A.; Rasch, P.

2012-12-01

Predictions of aerosol radiative forcing in climate models still contain large uncertainties, resulting from a poor understanding of certain aerosol processes, the level of complexity of aerosol processes represented in models, and the ability of models to account for sub-grid scale variability of aerosols and processes affecting them. In addition, comparing the performance and computational efficiency of new aerosol process modules used in various studies is problematic because different studies often employ different grid configurations, meteorology, trace gas chemistry, and emissions that affect the temporal and spatial evolution of aerosols. To address this issue, we have developed an Aerosol Modeling Testbed (AMT) to systematically and objectively evaluate aerosol process modules. The AMT consists of the modular Weather Research and Forecasting (WRF) model, a series of testbed cases for which extensive in situ and remote sensing measurements of meteorological, trace gas, and aerosol properties are available, and a suite of tools to evaluate the performance of meteorological, chemical, aerosol process modules. WRF contains various parameterizations of meteorological, chemical, and aerosol processes and includes interactive aerosol-cloud-radiation treatments similar to those employed by climate models. In addition, the physics suite from a global climate model, Community Atmosphere Model version 5 (CAM5), has also been ported to WRF so that these parameterizations can be tested at various spatial scales and compared directly with field campaign data and other parameterizations commonly used by the mesoscale modeling community. In this study, we evaluate simple and complex treatments of the aerosol size distribution and secondary organic aerosols using the AMT and measurements collected during three field campaigns: the Megacities Initiative Local and Global Observations (MILAGRO) campaign conducted in the vicinity of Mexico City during March 2006, the Carbonaceous Aerosol and Radiative Effects Study (CARES) conducted in the vicinity of Sacramento California during June 2010, and the California Nexus (CalNex) campaign conducted in southern California during May and June of 2010. For the aerosol size distribution, we compare the predictions from the GOCART bulk aerosol model, the MADE/SORGAM modal aerosol model, the Modal Aerosol Model (MAM) employed by CAM5, and the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) which uses a sectional representation. For secondary organic aerosols, we compare simple fixed mass yield approaches with the numerically complex volatility basis set approach. All simulations employ the same emissions, meteorology, trace gas chemistry (except for that involving condensable organic species), and initial and boundary conditions. Performance metrics from the AMT are used to assess performance in terms of simulated mass, composition, size distribution (except for GOCART), and aerosol optical properties in relation to computational expense. In addition to statistical measures, qualitative differences among the different aerosol models over the computational domain are presented to examine variations in how aerosols age among the aerosol models.

A Case Study of the Weather Research and Forecasting Model Applied to the Joint Urban 2003 Tracer Field Experiment. Part 1. Wind and Turbulence

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nelson, Matthew A.; Brown, Michael J.; Halverson, Scot A.

We found that numerical-weather-prediction models are often used to supply the mean wind and turbulence fields for atmospheric transport and dispersion plume models as they provide dense horizontally- and vertically-resolved geographic coverage in comparison to typically sparse monitoring networks. Here, the Weather Research and Forecasting (WRF) model was run over the month-long period of the Joint Urban 2003 field campaign conducted in Oklahoma City and the simulated fields important to transport and dispersion models were compared to measurements from a number of sodars, tower-based sonic anemometers, and balloon soundings located in the greater metropolitan area. Time histories of computed windmore » speed, wind direction, turbulent kinetic energy (e), friction velocity (u* ), and reciprocal Obukhov length (1 / L) were compared to measurements over the 1-month field campaign. Vertical profiles of wind speed, potential temperature (θ ), and e were compared during short intensive operating periods. The WRF model was typically able to replicate the measured diurnal variation of the wind fields, but with an average absolute wind direction and speed difference of 35° and 1.9 m s -1 , respectively. Then, using the Mellor-Yamada-Janjic (MYJ) surface-layer scheme, the WRF model was found to generally underpredict surface-layer TKE but overpredict u* that was observed above a suburban region of Oklahoma City. The TKE-threshold method used by the WRF model’s MYJ surface-layer scheme to compute the boundary-layer height (h) consistently overestimated h derived from a θ gradient method whether using observed or modelled θ profiles.« less

A Case Study of the Weather Research and Forecasting Model Applied to the Joint Urban 2003 Tracer Field Experiment. Part 1. Wind and Turbulence

DOE PAGES

Nelson, Matthew A.; Brown, Michael J.; Halverson, Scot A.; ...

2015-09-25

We found that numerical-weather-prediction models are often used to supply the mean wind and turbulence fields for atmospheric transport and dispersion plume models as they provide dense horizontally- and vertically-resolved geographic coverage in comparison to typically sparse monitoring networks. Here, the Weather Research and Forecasting (WRF) model was run over the month-long period of the Joint Urban 2003 field campaign conducted in Oklahoma City and the simulated fields important to transport and dispersion models were compared to measurements from a number of sodars, tower-based sonic anemometers, and balloon soundings located in the greater metropolitan area. Time histories of computed windmore » speed, wind direction, turbulent kinetic energy (e), friction velocity (u* ), and reciprocal Obukhov length (1 / L) were compared to measurements over the 1-month field campaign. Vertical profiles of wind speed, potential temperature (θ ), and e were compared during short intensive operating periods. The WRF model was typically able to replicate the measured diurnal variation of the wind fields, but with an average absolute wind direction and speed difference of 35° and 1.9 m s -1 , respectively. Then, using the Mellor-Yamada-Janjic (MYJ) surface-layer scheme, the WRF model was found to generally underpredict surface-layer TKE but overpredict u* that was observed above a suburban region of Oklahoma City. The TKE-threshold method used by the WRF model’s MYJ surface-layer scheme to compute the boundary-layer height (h) consistently overestimated h derived from a θ gradient method whether using observed or modelled θ profiles.« less

Appraisal of Weather Research and Forecasting Model Downscaling of Hydro-meteorological Variables and their Applicability for Discharge Prediction: Prognostic Approach for Ungauged Basin

NASA Astrophysics Data System (ADS)

Srivastava, P. K.; Han, D.; Rico-Ramirez, M. A.; Bray, M.; Islam, T.; Petropoulos, G.; Gupta, M.

2015-12-01

Hydro-meteorological variables such as Precipitation and Reference Evapotranspiration (ETo) are the most important variables for discharge prediction. However, it is not always possible to get access to them from ground based measurements, particularly in ungauged catchments. The mesoscale model WRF (Weather Research & Forecasting model) can be used for prediction of hydro-meteorological variables. However, hydro-meteorologists would like to know how well the downscaled global data products are as compared to ground based measurements and whether it is possible to use the downscaled data for ungauged catchments. Even with gauged catchments, most of the stations have only rain and flow gauges installed. Measurements of other weather hydro-meteorological variables such as solar radiation, wind speed, air temperature, and dew point are usually missing and thus complicate the problems. In this study, for downscaling the global datasets, the WRF model is setup over the Brue catchment with three nested domains (D1, D2 and D3) of horizontal grid spacing of 81 km, 27 km and 9 km are used. The hydro-meteorological variables are downscaled using the WRF model from the National Centers for Enviromental Prediction (NCEP) reanalysis datasets and subsequently used for the ETo estimation using the Penman Monteith equation. The analysis of weather variables and precipitation are compared against the ground based datasets, which indicate that the datasets are in agreement with the observed datasets for complete monitoring period as well as during the seasons except precipitation whose performance is poorer in comparison to the measured rainfall. After a comparison, the WRF estimated precipitation and ETo are then used as a input parameter in the Probability Distributed Model (PDM) for discharge prediction. The input data and model parameter sensitivity analysis and uncertainty estimation are also taken into account for the PDM calibration and prediction following the Generalised Likelihood Uncertainty Estimation (GLUE) approach. The overall analysis suggests that the uncertainty estimates in predicted discharge using WRF downscaled ETo have comparable performance to ground based observed datasets and hence is promising for discharge prediction in the absence of ground based measurements.

Sensitivity of an Integrated Mesoscale Atmosphere and Agriculture Land Modeling System (WRF/CMAQ-EPIC) to MODIS Vegetation and Lightning Assimilation

NASA Astrophysics Data System (ADS)

Ran, L.; Cooter, E. J.; Gilliam, R. C.; Foroutan, H.; Kang, D.; Appel, W.; Wong, D. C.; Pleim, J. E.; Benson, V.; Pouliot, G.

2017-12-01

The combined meteorology and air quality modeling system composed of the Weather Research and Forecast (WRF) model and Community Multiscale Air Quality (CMAQ) model is an important decision support tool that is used in research and regulatory decisions related to emissions, meteorology, climate, and chemical transport. The Environmental Policy Integrated Climate (EPIC) is a cropping model which has long been used in a range of applications related to soil erosion, crop productivity, climate change, and water quality around the world. We have integrated WRF/CMAQ with EPIC using the Fertilizer Emission Scenario Tool for CMAQ (FEST-C) to estimate daily soil N information with fertilization for CMAQ bi-directional ammonia flux modeling. Driven by the weather and N deposition from WRF/CMAQ, FEST-C EPIC simulations are conducted on 22 different agricultural production systems ranging from managed grass lands (e.g. hay and alfalfa) to crop lands (e.g. corn grain and soybean) with rainfed and irrigated information across any defined conterminous United States (U.S.) CMAQ domain and grid resolution. In recent years, this integrated system has been enhanced and applied in many different air quality and ecosystem assessment projects related to land-water-atmosphere interactions. These enhancements have advanced this system to become a valuable tool for integrated assessments of air, land and water quality in light of social drivers and human and ecological outcomes. This presentation will focus on evaluating the sensitivity of precipitation and N deposition in the integrated system to MODIS vegetation input and lightning assimilation and their impacts on agricultural production and fertilization. We will describe the integrated modeling system and evaluate simulated precipitation and N deposition along with other weather information (e.g. temperature, humidity) for 2011 over the conterminous U.S. at 12 km grids from a coupled WRF/CMAQ with MODIS and lightning assimilation. Simulated agricultural production and fertilization from FEST-C EPIC driven by the changed meteorology and N deposition from MODIS and lightning assimilations will be evaluated and analyzed.

Empowering Geoscience with Improved Data Assimilation Using the Data Assimilation Research Testbed "Manhattan" Release.

NASA Astrophysics Data System (ADS)

Raeder, K.; Hoar, T. J.; Anderson, J. L.; Collins, N.; Hendricks, J.; Kershaw, H.; Ha, S.; Snyder, C.; Skamarock, W. C.; Mizzi, A. P.; Liu, H.; Liu, J.; Pedatella, N. M.; Karspeck, A. R.; Karol, S. I.; Bitz, C. M.; Zhang, Y.

2017-12-01

The capabilities of the Data Assimilation Research Testbed (DART) at NCAR have been significantly expanded with the recent "Manhattan" release. DART is an ensemble Kalman filter based suite of tools, which enables researchers to use data assimilation (DA) without first becoming DA experts. Highlights: significant improvement in efficient ensemble DA for very large models on thousands of processors, direct read and write of model state files in parallel, more control of the DA output for finer-grained analysis, new model interfaces which are useful to a variety of geophysical researchers, new observation forward operators and the ability to use precomputed forward operators from the forecast model. The new model interfaces and example applications include the following: MPAS-A; Model for Prediction Across Scales - Atmosphere is a global, nonhydrostatic, variable-resolution mesh atmospheric model, which facilitates multi-scale analysis and forecasting. The absence of distinct subdomains eliminates problems associated with subdomain boundaries. It demonstrates the ability to consistently produce higher-quality analyses than coarse, uniform meshes do. WRF-Chem; Weather Research and Forecasting + (MOZART) Chemistry model assimilates observations from FRAPPÉ (Front Range Air Pollution and Photochemistry Experiment). WACCM-X; Whole Atmosphere Community Climate Model with thermosphere and ionosphere eXtension assimilates observations of electron density to investigate sudden stratospheric warming. CESM (weakly) coupled assimilation; NCAR's Community Earth System Model is used for assimilation of atmospheric and oceanic observations into their respective components using coupled atmosphere+land+ocean+sea+ice forecasts. CESM2.0; Assimilation in the atmospheric component (CAM, WACCM) of the newly released version is supported. This version contains new and extensively updated components and software environment. CICE; Los Alamos sea ice model (in CESM) is used to assimilate multivariate sea ice concentration observations to constrain the model's ice thickness, concentration, and parameters.

Comparison of Thunderstorm Simulations from WRF-NMM and WRF-ARW Models over East Indian Region

PubMed Central

Litta, A. J.; Mary Ididcula, Sumam; Mohanty, U. C.; Kiran Prasad, S.

2012-01-01

The thunderstorms are typical mesoscale systems dominated by intense convection. Mesoscale models are essential for the accurate prediction of such high-impact weather events. In the present study, an attempt has been made to compare the simulated results of three thunderstorm events using NMM and ARW model core of WRF system and validated the model results with observations. Both models performed well in capturing stability indices which are indicators of severe convective activity. Comparison of model-simulated radar reflectivity imageries with observations revealed that NMM model has simulated well the propagation of the squall line, while the squall line movement was slow in ARW. From the model-simulated spatial plots of cloud top temperature, we can see that NMM model has better captured the genesis, intensification, and propagation of thunder squall than ARW model. The statistical analysis of rainfall indicates the better performance of NMM than ARW. Comparison of model-simulated thunderstorm affected parameters with that of the observed showed that NMM has performed better than ARW in capturing the sharp rise in humidity and drop in temperature. This suggests that NMM model has the potential to provide unique and valuable information for severe thunderstorm forecasters over east Indian region. PMID:22645480

Strategies for Teaching Regional Climate Modeling: Online Professional Development for Scientists and Decision Makers

NASA Astrophysics Data System (ADS)

Walton, P.; Yarker, M. B.; Mesquita, M. D. S.; Otto, F. E. L.

2014-12-01

There is a clear role for climate science in supporting decision making at a range of scales and in a range of contexts: from Global to local, from Policy to Industry. However, clear a role climate science can play, there is also a clear discrepancy in the understanding of how to use the science and associated tools (such as climate models). Despite there being a large body of literature on the science there is clearly a need to provide greater support in how to apply appropriately. However, access to high quality professional development courses can be problematic, due to geographic, financial and time constraints. In attempt to address this gap we independently developed two online professional courses that focused on helping participants use and apply two regional climate models, WRF and PRECIS. Both courses were designed to support participants' learning through tutor led programs that covered the basic climate scientific principles of regional climate modeling and how to apply model outputs. The fundamental differences between the two courses are: 1) the WRF modeling course expected participants to design their own research question that was then run on a version of the model, whereas 2) the PRECIS course concentrated on the principles of regional modeling and how the climate science informed the modeling process. The two courses were developed to utilise the cost and time management benefits associated with eLearning, with the recognition that this mode of teaching can also be accessed internationally, providing professional development courses in countries that may not be able to provide their own. The development teams saw it as critical that the courses reflected sound educational theory, to ensure that participants had the maximum opportunity to learn successfully. In particular, the role of reflection is central to both course structures to help participants make sense of the science in relation to their own situation. This paper details the different structures of both courses, evaluating the advantages and disadvantages of each, along with the educational approaches used. We conclude by proposing a framework for the develop of educationally robust online professional development programs that actively supports decision makers in understanding, developing and applying regional climate models.

Development of a short-term irradiance prediction system using post-processing tools on WRF-ARW meteorological forecasts in Spain

NASA Astrophysics Data System (ADS)

Rincón, A.; Jorba, O.; Baldasano, J. M.

2010-09-01

The increased contribution of solar energy in power generation sources requires an accurate estimation of surface solar irradiance conditioned by geographical, temporal and meteorological conditions. The knowledge of the variability of these factors is essential to estimate the expected energy production and therefore help stabilizing the electricity grid and increase the reliability of available solar energy. The use of numerical meteorological models in combination with statistical post-processing tools may have the potential to satisfy the requirements for short-term forecasting of solar irradiance for up to several days ahead and its application in solar devices. In this contribution, we present an assessment of a short-term irradiance prediction system based on the WRF-ARW mesoscale meteorological model (Skamarock et al., 2005) and several post-processing tools in order to improve the overall skills of the system in an annual simulation of the year 2004 in Spain. The WRF-ARW model is applied with 4 km x 4 km horizontal resolution and 38 vertical layers over the Iberian Peninsula. The hourly model irradiance is evaluated against more than 90 surface stations. The stations are used to assess the temporal and spatial fluctuations and trends of the system evaluating three different post-processes: Model Output Statistics technique (MOS; Glahn and Lowry, 1972), Recursive statistical method (REC; Boi, 2004) and Kalman Filter Predictor (KFP, Bozic, 1994; Roeger et al., 2003). A first evaluation of the system without post-processing tools shows an overestimation of the surface irradiance, due to the lack of atmospheric absorbers attenuation different than clouds not included in the meteorological model. This produces an annual BIAS of 16 W m-2 h-1, annual RMSE of 106 W m-2 h-1 and annual NMAE of 42%. The largest errors are observed in spring and summer, reaching RMSE of 350 W m-2 h-1. Results using Kalman Filter Predictor show a reduction of 8% of RMSE, 83% of BIAS, and NMAE decreases down to 32%. The REC method shows a reduction of 6% of RMSE, 79% of BIAS, and NMAE decreases down to 28%. When comparing stations at different altitudes, the overestimation is enhanced at coastal stations (less than 200m) up to 900 W m-2 h-1. The results allow us to analyze strengths and drawbacks of the irradiance prediction system and its application in the estimation of energy production from photovoltaic system cells. References Boi, P.: A statistical method for forecasting extreme daily temperatures using ECMWF 2-m temperatures and ground station measurements, Meteorol. Appl., 11, 245-251, 2004. Bozic, S.: Digital and Kalman filtering, John Wiley, Hoboken, New Jersey, 2nd edn., 1994. Glahn, H. and Lowry, D.: The use of Model Output Statistics (MOS) in Objective Weather Forecasting, Applied Meteorology, 11, 1203-1211, 1972. Roeger, C., Stull, R., McClung, D., Hacker, J., Deng, X., and Modzelewski, H.: Verification of Mesoscale Numerical Weather Forecasts in Mountainous Terrain for Application to Avalanche Prediction, Weather and forecasting, 18, 1140-1160, 2003. Skamarock, W., Klemp, J., Dudhia, J., Gill, D., Barker, D. M., Wang, W., and Powers, J. G.: A Description of the Advanced Research WRF Version 2, Tech. Rep. NCAR/TN-468+STR, NCAR Technical note, 2005.

Worsening renal function defined as an absolute increase in serum creatinine is a biased metric for the study of cardio-renal interactions.

PubMed

Testani, Jeffrey M; McCauley, Brian D; Chen, Jennifer; Shumski, Michael; Shannon, Richard P

2010-01-01

Worsening renal function (WRF) during the treatment of decompensated heart failure, frequently defined as an absolute increase in serum creatinine >or=0.3 mg/dl, has been reported as a strong adverse prognostic factor in several studies. We hypothesized that this definition of WRF is biased by baseline renal function secondary to the exponential relationship between creatinine and renal function. We reviewed consecutive admissions with a discharge diagnosis of heart failure. An increase in creatinine >or=0.3 mg/dl (WRF(CREAT)) was compared to a decrease in GFR >or=20% (WRF(GFR)). Overall, 993 admissions met eligibility. WRF(CREAT) occurred in 31.5% and WRF(GFR) in 32.7%. WRF(CREAT) and WRF(GFR) had opposing relationships with baseline renal function (OR = 1.9 vs. OR = 0.51, respectively, p < 0.001). Both definitions had similar unadjusted associations with death at 30 days [WRF(GFR) OR = 2.3 (95% CI 1.1-4.8), p = 0.026; WRF(CREAT) OR = 2.1 (95% CI 1.0-4.4), p = 0.047]. Controlling for baseline renal insufficiency, WRF(GFR) added incrementally in the prediction of mortality (p = 0.009); however, WRF(CREAT) did not (p = 0.11). WRF, defined as an absolute change in serum creatinine, is heavily biased by baseline renal function. An alternative definition of WRF should be considered for future studies of cardio-renal interactions. Copyright 2010 S. Karger AG, Basel.

Comparing Lagrangian and Eulerian models for CO2 transport - a step towards Bayesian inverse modeling using WRF/STILT-VPRM

NASA Astrophysics Data System (ADS)

Pillai, D.; Gerbig, C.; Kretschmer, R.; Beck, V.; Karstens, U.; Neininger, B.; Heimann, M.

2012-01-01

We present simulations of atmospheric CO2 concentrations provided by two modeling systems, run at high spatial resolution: the Eulerian-based Weather Research Forecasting (WRF) model and the Lagrangian-based Stochastic Time-Inverted Lagrangian Transport (STILT) model, both of which are coupled to a diagnostic biospheric model, the Vegetation Photosynthesis and Respiration Model (VPRM). The consistency of the simulations is assessed with special attention paid to the details of horizontal as well as vertical transport and mixing of CO2 concentrations in the atmosphere. The dependence of model mismatch (Eulerian vs. Lagrangian) on models' spatial resolution is further investigated. A case study using airborne measurements during which both models showed large deviations from each other is analyzed in detail as an extreme case. Using aircraft observations and pulse release simulations, we identified differences in the representation of details in the interaction between turbulent mixing and advection through wind shear as the main cause of discrepancies between WRF and STILT transport at a spatial resolution such as 2 and 6 km. Based on observations and inter-model comparisons of atmospheric CO2 concentrations, we show that a refinement of the parameterization of turbulent velocity variance and Lagrangian time-scale in STILT is needed to achieve a better match between the Eulerian and the Lagrangian transport at such a high spatial resolution (e.g. 2 and 6 km). Nevertheless, the inter-model differences in simulated CO2 time series for a tall tower observatory at Ochsenkopf in Germany are about a factor of two smaller than the model-data mismatch and about a factor of three smaller than the mismatch between the current global model simulations and the data. Thus suggests that it is reasonable to use STILT as an adjoint model of WRF atmospheric transport.

On-Treatment Outcomes in Patients With Worsening Renal Function With Rivaroxaban Compared With Warfarin: Insights From ROCKET AF.

PubMed

Fordyce, Christopher B; Hellkamp, Anne S; Lokhnygina, Yuliya; Lindner, Samuel M; Piccini, Jonathan P; Becker, Richard C; Berkowitz, Scott D; Breithardt, Günter; Fox, Keith A A; Mahaffey, Kenneth W; Nessel, Christopher C; Singer, Daniel E; Patel, Manesh R

2016-07-05

Despite rapid clinical adoption of novel anticoagulants, it is unknown whether outcomes differ among patients with worsening renal function (WRF) taking these new drugs compared with warfarin. We aimed to determine whether the primary efficacy (stroke or systemic embolism) and safety (major bleeding and nonmajor clinically relevant bleeding) end points from the ROCKET AF trial (Rivaroxaban Once-Daily, Oral, Direct Factor Xa Inhibition Compared With Vitamin K Antagonism for Prevention of Stroke and Embolism Trial in Atrial Fibrillation trial) differed among participants with WRF taking rivaroxaban and those taking warfarin. After excluding patients without at least 1 follow-up creatinine measurement (n=1624), we included all remaining patients (n=12 612) randomly assigned to either rivaroxaban or dose-adjusted warfarin. On-treatment WRF (a decrease of >20% from screening creatinine clearance measurement at any time point during the study) was evaluated as a time-dependent covariate in Cox proportional hazards models. Baseline characteristics were generally similar between patients with stable renal function (n=9292) and WRF (n=3320). Rates of stroke or systemic embolism, myocardial infarction, and bleeding were also similar, but WRF patients experienced a higher incidence of vascular death versus stable renal function (2.21 versus 1.41 events per 100 patient-years; P=0.026). WRF patients who were randomized to receive rivaroxaban had a reduction in stroke or systemic embolism compared with those taking warfarin (1.54 versus 3.25 events per 100 patient-years) that was not seen in patients with stable renal function who were randomized to receive rivaroxaban (P=0.050 for interaction). There was no difference in major or nonmajor clinically relevant bleeding among WRF patients randomized to warfarin versus rivaroxaban. Among patients with on-treatment WRF, rivaroxaban was associated with lower rates of stroke and systemic embolism compared with warfarin, without an increase in the composite bleeding end point. URL: http://www.clinicaltrials.gov. Unique identifier: NCT00403767. © 2016 American Heart Association, Inc.

Toward Improved Land Surface Initialization in Support of Regional WRF Forecasts at the Kenya Meteorological Service (KMS)

NASA Technical Reports Server (NTRS)

Case, Johnathan L.; Mungai, John; Sakwa, Vincent; Kabuchanga, Eric; Zavodsky, Bradley T.; Limaye, Ashutosh S.

2014-01-01

Flooding and drought are two key forecasting challenges for the Kenya Meteorological Service (KMS). Atmospheric processes leading to excessive precipitation and/or prolonged drought can be quite sensitive to the state of the land surface, which interacts with the planetary boundary layer (PBL) of the atmosphere providing a source of heat and moisture. The development and evolution of precipitation systems are affected by heat and moisture fluxes from the land surface, particularly within weakly-sheared environments such as in the tropics and sub-tropics. These heat and moisture fluxes during the day can be strongly influenced by land cover, vegetation, and soil moisture content. Therefore, it is important to represent the land surface state as accurately as possible in land surface and numerical weather prediction (NWP) models. Enhanced regional modeling capabilities have the potential to improve forecast guidance in support of daily operations and high-impact weather over eastern Africa. KMS currently runs a configuration of the Weather Research and Forecasting (WRF) NWP model in real time to support its daily forecasting operations, making use of the NOAA/National Weather Service (NWS) Science and Training Resource Center's Environmental Modeling System (EMS) to manage and produce the KMS-WRF runs on a regional grid over eastern Africa. Two organizations at the NASA Marshall Space Flight Center in Huntsville, AL, SERVIR and the Shortterm Prediction Research and Transition (SPoRT) Center, have established a working partnership with KMS for enhancing its regional modeling capabilities through new datasets and tools. To accomplish this goal, SPoRT and SERVIR is providing enhanced, experimental land surface initialization datasets and model verification capabilities to KMS as part of this collaboration. To produce a land-surface initialization more consistent with the resolution of the KMS-WRF runs, the NASA Land Information System (LIS) is run at a comparable resolution to provide real-time, daily soil initialization data in place of data interpolated from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model soil moisture and temperature fields. Additionally, realtime green vegetation fraction (GVF) data from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (Suomi- NPP) satellite will be incorporated into the KMS-WRF runs, once it becomes publicly available from the National Environmental Satellite Data and Information Service (NESDIS). Finally, model verification capabilities will be transitioned to KMS using the Model Evaluation Tools (MET; Brown et al. 2009) package in conjunction with a dynamic scripting package developed by SPoRT (Zavodsky et al. 2014), to help quantify possible improvements in simulated temperature, moisture and precipitation resulting from the experimental land surface initialization. Furthermore, the transition of these MET tools will enable KMS to monitor model forecast accuracy in near real time. This paper presents preliminary efforts to improve land surface model initialization over eastern Africa in support of operations at KMS. The remainder of this extended abstract is organized as follows: The collaborating organizations involved in the project are described in Section 2; background information on LIS and the configuration for eastern Africa is presented in Section 3; the WRF configuration used in this modeling experiment is described in Section 4; sample experimental WRF output with and without LIS initialization data are given in Section 5; a summary is given in Section 6 followed by acknowledgements and references.