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

Xu, Jianjun; Rugg, Steven; Byerle, Lee A.; Liu, Zhiquan

doi:10.1175/2009waf2222229.1

Cited by 23 publications

(17 citation statements)

References 31 publications

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“…The largest temperature biases are found for the highest elevations, which is also consistent with the findings in other mesoscale model experiments. For example, Xu et al (2009) examined the WRF-ARW model biases in the complex terrain of Southwest Asia from 1 to 31 May 2006. The 2-m temperature errors in their simulations were closely related to the heterogeneity in terrain structure, with larger forecast errors located in the higher elevation terrain.…”

Section: Dependence Of Biases With Terrain Height For Different Forecmentioning

confidence: 99%

“…By taking advantage of the new observational technologies and advanced data assimilation techniques, e.g., an off-line high-resolution land-surface model (LSM) spun-up (Case et al 2008), or the initialization of the model through assimilation of diverse observations, such as satellite radiance data (Xu et al 2009), or tropospheric airborne meteorological data reporting (TAMDAR) measurements , the model-forecast errors could be further mitigated.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of the surface temperature and wind forecast errors of the NCAR-AirDat operational CONUS 4-km WRF forecasting system

Wyszogrodzki

Liu

Jacobs³

et al. 2013

Meteorol Atmos Phys

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Investigating the characteristics of modelforecast errors using various statistical and object-oriented methods is necessary for providing useful guidance to endusers and model developers as well. To this end, the random and systematic errors (i.e., biases) of the 2-m temperature and 10-m wind predictions of the NCAR-AirDat weather research and forecasting (WRF)-based real-time four-dimensional data assimilation (RTFDDA) and forecasting system are analyzed. This system has been running operationally over a contiguous United States (CONUS) domain at a 4-km grid spacing with four forecast cycles daily from June 2009 to September 2010. In the result an exceptionally useful forecast dataset was generated and used for studying the error properties of the model forecasts, in terms of both a longer time period and a broader coverage of geographic regions than previously studied. Spatiotemporal characteristics of the errors are investigated based on the 24-h forecasts between June 2009 and April 2010, and the 72-h forecasts between May and September 2010. It was found that the biases of both wind and temperature forecasts vary greatly seasonally and diurnally, with dependency on the forecast length, station elevation, geographical location, and meteorological conditions. The temperature showed systematic cold biases during the daytime at all station elevations and warm biases during the nighttime above 1,000 m above sea level (ASL), while below 600 m ASL cold biases occurred during the nighttime. The forecasts of surface wind speed exhibited strong positive biases during the nighttime, while the negative biases were observed in the spring and summer afternoons. The surface wind speed was mostly over-predicted except for the stations located between 1,000 and 2,100 m ASL, for which negative biases were identified for most forecast cycles. The highest wind-speed errors were found over the high terrain and near sea-level stations. The wind-direction errors were relatively large at the high-terrain elevation in the Rocky and Appalachian mountain ranges and the western coastal areas and the error structure exhibited notable diurnal variability.

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Section: Dependence Of Biases With Terrain Height For Different Forecmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of the surface temperature and wind forecast errors of the NCAR-AirDat operational CONUS 4-km WRF forecasting system

Wyszogrodzki

Liu

Jacobs³

et al. 2013

Meteorol Atmos Phys

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“…The four layers include lower troposphere (LT) from 800 to 1000 hPa, middle troposphere (MT) from 400 to 800 hPa, upper troposphere (UT) from 200 to 400 hPa and lower stratosphere (LS) from 50 to 200 hPa. Similar to a previous study (Xu, et al, 2009), two statistical variables -bias and root mean square errors (RMSEs) -are investigated. If X represents any of the parameters under consideration for a given time and vertical level, then the forecast error is defined as X = X f −X o , where the subscripts f and o denote forecast and observed quantities, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…In the ARW model, the physics of the model includes the Goddard Cumulus Ensemble (GCE) microphysics scheme, Yonsei University planetary boundary layer (PBL) scheme, Noah land surface model, Rapid Radiative Transfer Model (RRTM) longwave radiation, and the Goddard shortwave radiation scheme (Xu et al, 2009). The 15 km WRF model forecast with a mesh size domain of 718 × 373 ( Fig.…”

Section: Experiments Designmentioning

confidence: 99%

Impacts of AMSU-A, MHS and IASI data assimilation on temperature and humidity forecasts with GSI–WRF over the western United States

Bao

Shao

et al. 2015

Atmos. Meas. Tech.

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Abstract. Using NOAA's Gridpoint Statistical Interpolation (GSI) data assimilation system and NCAR's Advanced Research WRF (Weather Research and Forecasting) (ARW-WRF) regional model, six experiments are designed by (1) a control experiment (CTRL) and five data assimilation (DA) experiments with different data sets, including (2) conventional data only (CON); (3) microwave data (AMSU-A + MHS) only (MW); (4) infrared data (IASI) only (IR); (5) a combination of microwave and infrared data (MWIR); and (6) a combination of conventional, microwave and infrared observation data (ALL). One-month experiments in July 2012 and the impacts of the DA on temperature and moisture forecasts at the surface and four vertical layers over the western United States have been investigated. The four layers include lower troposphere (LT) from 800 to 1000 hPa, middle troposphere (MT) from 400 to 800 hPa, upper troposphere (UT) from 200 to 400 hPa, and lower stratosphere (LS) from 50 to 200 hPa. The results show that the regional GSI-WRF system is underestimating the observed temperature in the LT and overestimating in the UT and LS. The MW DA reduced the forecast bias from the MT to the LS within 30 h forecasts, and the CON DA kept a smaller forecast bias in the LT for 2-day forecasts. The largest root mean square error (RMSE) is observed in the LT and at the surface (SFC). Compared to the CTRL, the MW DA produced the most positive contribution in the UT and LS, and the CON DA mainly improved the temperature forecasts at the SFC. However, the IR DA gave a negative contribution in the LT.Most of the observed humidity in the different vertical layers is overestimated in the humidity forecasts except in the UT. The smallest bias in the humidity forecast occurred at the SFC and in the UT. The DA experiments apparently reduced the bias from the LT to UT, especially for the IR DA experiment, but the RMSEs are not reduced in the humidity forecasts. Compared to the CTRL, the IR DA experiment has a larger RMSE in the moisture forecast, although the smallest bias is found in the LT and MT.

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“…Their calculation equations can be found in many published materials, such as Lo et al (2008) and Xu et al (2009). Actually, these three parameters can be used to assess the model performance from different aspects.…”

Section: Statistical Verification Techniquesmentioning

confidence: 99%

Validating the dynamic downscaling ability of WRF for East Asian summer climate

Gao

Hou

Xue

et al. 2015

Theor Appl Climatol

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To better understand the regional climate model (RCM) performance for East Asian summer climate and the influencing factors, this study evaluated the dynamic downscaling ability of the Weather Research Forecast (WRF) RCM. According to the comprehensive comparison studies on different physical processes and experimental settings, the optimal combination of WRF model setups can be obtained for East Asian precipitation and temperature simulations. Furthermore, based on the optimal combination, when compared with climate observations, WRF shows high ability to downscale NCEP DOE Reanalysis-2, which provided initial and lateral boundary conditions for the WRF, especially for the precipitation simulation due to the better simulated lowlevel water vapor flux. However, the strengthened Western North Pacific Subtropical High (WPSH) from WRF simulation results in the positive anomaly for summer rainfall.

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

Cited by 23 publications

References 31 publications

Analysis of the surface temperature and wind forecast errors of the NCAR-AirDat operational CONUS 4-km WRF forecasting system

Analysis of the surface temperature and wind forecast errors of the NCAR-AirDat operational CONUS 4-km WRF forecasting system

Impacts of AMSU-A, MHS and IASI data assimilation on temperature and humidity forecasts with GSI–WRF over the western United States

Validating the dynamic downscaling ability of WRF for East Asian summer climate

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