scholarly journals Adding Four Dimensional Data Assimilation by Analysis Nudging to the Model for Prediction Across Scales – Atmosphere (Version 4.0)

2017 ◽  
Author(s):  
Orren Russell Bullock Jr. ◽  
Hosein Foroutan ◽  
Robert C. Gilliam ◽  
Jerold A. Herwehe
Keyword(s):  
Data Assimilation ◽  
Mesoscale Model ◽  
Global Analysis ◽  
Analysis Data ◽  
Global Scale ◽  
Model Errors ◽  
Near Surface ◽  
Target Values ◽  
Four Dimensional Data Assimilation ◽  
Reference Fields

Abstract. The Model for Prediction Across Scales – Atmosphere (MPAS-A) has been modified to allow four dimensional data assimilation (FDDA) by the nudging of temperature, humidity and wind toward target values predefined on the MPAS-A computational mesh. The addition of nudging allows MPAS-A to be used as a global-scale meteorological driver for retrospective air quality modeling. The technique of analysis nudging developed for the Penn State / NCAR Mesoscale Model, and later applied in the Weather Research and Forecasting model, is implemented in MPAS-A with adaptations for its unstructured Voronoi mesh. Reference fields generated from 1° × 1° National Centers for Environmental Prediction FNL (Final) Operational Global Analysis data were used to constrain MPAS-A simulations on a 92–25 km variable-resolution mesh with refinement centered over the contiguous United States. Test simulations were conducted for January and July 2013 with and without FDDA, and compared to reference fields and near-surface meteorological observations. The results demonstrate that MPAS-A with analysis nudging has high fidelity to the reference data while still maintaining conservation of mass as in the unmodified model. The results also show that application of FDDA constrains model errors relative to 2 m temperature, 2 m water vapor mixing ratio, and 10 m wind speed such that they continue to be at or below the magnitudes found at the start of each test period.

scholarly journals Adding four-dimensional data assimilation by analysis nudging to the Model for Prediction Across Scales – Atmosphere (version 4.0)

2018 ◽  
Vol 11 (7) ◽  
pp. 2897-2922 ◽  
Author(s):  
Orren Russell Bullock Jr. ◽  
Hosein Foroutan ◽  
Robert C. Gilliam ◽  
Jerold A. Herwehe
Keyword(s):  
Data Assimilation ◽  
Mesoscale Model ◽  
Global Analysis ◽  
Analysis Data ◽  
Global Scale ◽  
Model Errors ◽  
Near Surface ◽  
Target Values ◽  
Four Dimensional Data Assimilation ◽  
Reference Fields

Abstract. The Model for Prediction Across Scales – Atmosphere (MPAS-A) has been modified to allow four-dimensional data assimilation (FDDA) by the nudging of temperature, humidity, and wind toward target values predefined on the MPAS-A computational mesh. The addition of nudging allows MPAS-A to be used as a global-scale meteorological driver for retrospective air quality modeling. The technique of “analysis nudging” developed for the Penn State/National Center for Atmospheric Research (NCAR) Mesoscale Model, and later applied in the Weather Research and Forecasting model, is implemented in MPAS-A with adaptations for its polygonal Voronoi mesh. Reference fields generated from 1∘ × 1∘ National Centers for Environmental Prediction (NCEP) FNL (Final) Operational Global Analysis data were used to constrain MPAS-A simulations on a 92–25 km variable-resolution mesh with refinement centered over the contiguous United States. Test simulations were conducted for January and July 2013 with and without FDDA, and compared to reference fields and near-surface meteorological observations. The results demonstrate that MPAS-A with analysis nudging has high fidelity to the reference data while still maintaining conservation of mass as in the unmodified model. The results also show that application of FDDA constrains model errors relative to 2 m temperature, 2 m water vapor mixing ratio, and 10 m wind speed such that they continue to be at or below the magnitudes found at the start of each test period.

Weather Forecasts by the WRF-ARW Model with the GSI Data Assimilation System in the Complex Terrain Areas of Southwest Asia

2009 ◽  
Vol 24 (4) ◽  
pp. 987-1008 ◽  
Author(s):  
J. Xu ◽  
S. Rugg ◽  
L. Byerle ◽  
Z. Liu
Keyword(s):  
Data Assimilation ◽  
Satellite Data ◽  
Diurnal Cycle ◽  
Complex Terrain ◽  
Initial Conditions ◽  
Forecast Errors ◽  
Model Errors ◽  
Southwest Asia ◽  
Model Forecast ◽  
Near Surface

Abstract This paper will first describe the forecasting errors encountered from running the National Center for Atmospheric Research (NCAR) mesoscale model (the Advanced Research Weather Research and Forecasting model; ARW) in the complex terrain of southwest Asia from 1 to 31 May 2006. The subsequent statistical evaluation is designed to assess the model’s surface and upper-air forecast accuracy. Results show that the model biases caused by inadequate parameterization of physical processes are relatively small, except for the 2-m temperature, as compared to the nonsystematic errors resulting in part from the uncertainty in the initial conditions. The total model forecast errors at the surface show a substantial spatial heterogeneity; the errors are relatively larger in higher mountain areas. The performance of 2-m temperature forecasts is different from the other surface variables’ forecasts; the model forecast errors in 2-m temperature forecasts are closely related to the terrain configuration. The diurnal cycle variation of these near-surface temperature forecasts from the model is much smaller than what is observed. Second, in order to understand the role of the initial conditions in relation to the accuracy of the model forecasts, this study assimilated a form of satellite radiance data into this model through the Joint Center for Satellite Data Assimilation (JCSDA) analysis system called the Gridpoint Statistical Interpolation (GSI). The results indicate that on average over a 30-day experiment for the 24- and 48-h (second 24 h) forecasts, the satellite data provide beneficial information for improving the initial conditions and the model errors are reduced to some degree over some of the study locations. The diurnal cycle for some forecasting variables can be improved after satellite data assimilation; however, the improvement is very limited.

scholarly journals On Improving 4-km Mesoscale Model Simulations

2006 ◽  
Vol 45 (3) ◽  
pp. 361-381 ◽  
Author(s):  
Aijun Deng ◽  
David R. Stauffer
Keyword(s):  
Mesoscale Model ◽  
Vertical Mixing ◽  
Horizontal Resolution ◽  
Tracer Experiment ◽  
State University ◽  
Model Errors ◽  
Low Level ◽  
Medium Range Forecast ◽  
Pbl Scheme ◽  
Four Dimensional Data Assimilation

Abstract A previous study showed that use of analysis-nudging four-dimensional data assimilation (FDDA) and improved physics in the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) produced the best overall performance on a 12-km-domain simulation, based on the 18–19 September 1983 Cross-Appalachian Tracer Experiment (CAPTEX) case. However, reducing the simulated grid length to 4 km had detrimental effects. The primary cause was likely the explicit representation of convection accompanying a cold-frontal system. Because no convective parameterization scheme (CPS) was used, the convective updrafts were forced on coarser-than-realistic scales, and the rainfall and the atmospheric response to the convection were too strong. The evaporative cooling and downdrafts were too vigorous, causing widespread disruption of the low-level winds and spurious advection of the simulated tracer. In this study, a series of experiments was designed to address this general problem involving 4-km model precipitation and gridpoint storms and associated model sensitivities to the use of FDDA, planetary boundary layer (PBL) turbulence physics, grid-explicit microphysics, a CPS, and enhanced horizontal diffusion. Some of the conclusions include the following: 1) Enhanced parameterized vertical mixing in the turbulent kinetic energy (TKE) turbulence scheme has shown marked improvements in the simulated fields. 2) Use of a CPS on the 4-km grid improved the precipitation and low-level wind results. 3) Use of the Hong and Pan Medium-Range Forecast PBL scheme showed larger model errors within the PBL and a clear tendency to predict much deeper PBL heights than the TKE scheme. 4) Combining observation-nudging FDDA with a CPS produced the best overall simulations. 5) Finer horizontal resolution does not always produce better simulations, especially in convectively unstable environments, and a new CPS suitable for 4-km resolution is needed. 6) Although use of current CPSs may violate their underlying assumptions related to the size of the convective element relative to the grid size, the gridpoint storm problem was greatly reduced by applying a CPS to the 4-km grid.

scholarly journals Global Soil Moisture Estimation by Assimilating AMSR-E Brightness Temperatures in a Coupled CLM4–RTM–DART System

2016 ◽  
Vol 17 (9) ◽  
pp. 2431-2454 ◽  
Author(s):  
Long Zhao ◽  
Zong-Liang Yang ◽  
Timothy J. Hoar
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Global Scale ◽  
Radiative Transfer Model ◽  
Time Shift ◽  
Transfer Model ◽  
Systematic Bias ◽  
Near Surface ◽  
Community Land Model ◽  
Model Version

Abstract Very few frameworks exist that estimate global-scale soil moisture through microwave land data assimilation (DA). Toward this goal, such a framework has been developed by linking the Community Land Model, version 4 (CLM4), and a microwave radiative transfer model (RTM) with the Data Assimilation Research Testbed (DART). The deterministic ensemble adjustment Kalman filter (EAKF) within DART is utilized to estimate global multilayer soil moisture by assimilating brightness temperature observations from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). A 40-member ensemble of Community Atmosphere Model, version 4.0 (CAM4.0), reanalysis is adopted to drive CLM4 simulations. Space-specific, time-invariant microwave parameters are precalibrated to minimize uncertainties in RTM. Besides, various methods are designed to upscale AMSR-E observations for computational efficiency and time shift CAM4.0 forcing to facilitate global daily assimilations. A series of experiments are conducted to quantify the DA sensitivity to microwave parameters, choice of assimilated observations, and different CLM4 updating schemes. Evaluation results indicate that the newly established CLM4–RTM–DART framework improves the open-loop CLM4-simulated soil moisture. Precalibrated microwave parameters, rather than their default values, can ensure a more robust global-scale performance. In addition, updating near-surface soil moisture is capable of improving soil moisture in deeper layers (0–30 cm), while simultaneously updating multilayer soil moisture fails to obtain intended improvements. Future work is needed to address the systematic bias in CLM4 that cannot be fully covered through the ensemble spread in CAM4.0 reanalysis.

The Use of Surface Observations in Four-Dimensional Data Assimilation Using a Mesoscale Model

1996 ◽  
Vol 124 (5) ◽  
pp. 1018-1033 ◽  
Author(s):  
Frank H. Ruggiero ◽  
Keith D. Sashegyi ◽  
Rangarao V. Madala ◽  
Sethu Raman
Keyword(s):  
Data Assimilation ◽  
Mesoscale Model ◽  
Four Dimensional Data Assimilation ◽  
Surface Observations
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