A Limited Area Modeling Capability for the Finite‐Volume Cubed‐Sphere (FV3) Dynamical Core and Comparison With a Global Two‐Way Nest

Black, Thomas L.; Abeles, J.; Blake, Benjamin T.; Rogers, Eric; Zhang, X.; Aligo, Eric; Dawson, Logan C.; Ying, Lin; Strobach, Edward; Shafran, P.; Carley, Jacob R.

doi:10.1029/2021ms002483

Cited by 18 publications

(6 citation statements)

References 45 publications

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“…The NWP module is built upon the Unified Forecast System Short-Range Weather (UFS-SRW) application version 1.0.0, which was first released in March 2021 [40]. The application includes a pre-processing utility, the FV3 Limited Area Model (LAM) [41], the Common Community Physics Package (CCPP) [42], and the Unified Post Processor (UPP). Already been chosen as the forecast module for NOAA's next-generation Rapid Refresh Forecast System (RRFS), performance evaluation studies have revealed its functionality and accuracy [43][44].…”

Section: Nwp Modulementioning

confidence: 99%

Assimilation of FY-3D and FY-3E Hyperspectral Infrared Atmospheric Sounding Observation and Its Impact on Numerical Weather Prediction during Spring Season over the Continental United States

Zhang

Shao

2023

Atmosphere

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As a part of the World Meteorological Organization (WMO) Global Observing System, HIRAS-1 and HIRAS-2’s observations’ impact on improving the accuracy of numerical weather prediction (NWP) can be summarized into two questions: (1) Will HIRAS observation help the NWP system to improve its accuracy? (2) Which instrument has the greater impact on NWP? To answer the questions, four experiments are designed here: (I) the HIRAS-1 experiment, which assimilates the principal component (PC) scores derived from HIRAS-1 radiance observation from the FY-3D satellite; (II) the HIRAS-2 experiment, which assimilates HIRAS-2 (onboard the FY-3E satellite) radiance-observation-derived PC scores; (III) the J-01 experiment, which assimilates JPSS1 CrIS radiance-observation-derived PC scores; (IV) the control experiment. Each experiment generated a series of forecasts with 24 h lead-time from 16 March 2022 to 12 April 2022 using the Unified Forecast System Short-Range Weather application. Forecast evaluation using radiosonde and aircraft observation reveals: (a) for upper-level variables (i.e., temperature and specific humidity), assimilating HIRAS observation can improve the NWP’s performance by decreasing the standard deviation (Stdev) and increasing the anomaly correlation coefficient (ACC); (b) according to the multi-category Heidke skill score, HIRAS assimilation experiments, especially the HIRAS-2 experiment, have a higher agreement with hourly precipitation observations; (c) based on two tornado-outbreak case studies, which occurred on 30 March 2022 and 5 April 2022, HIRAS observation can increase the predicted intensity of 0–1 km storm relative helicity and decrease the height of the lifted condensation level at tornado outbreak locations; and (d) compared to CrIS, HIRAS-2 still has room for improvement.

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Section: Nwp Modulementioning

confidence: 99%

Assimilation of FY-3D and FY-3E Hyperspectral Infrared Atmospheric Sounding Observation and Its Impact on Numerical Weather Prediction during Spring Season over the Continental United States

Zhang

Shao

2023

Atmosphere

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“…Although JEDI also supports the limited area model version of FV3 (FV3‐LAM, Black et al., 2021), capabilities to assimilate regional data sets within JEDI for regional applications are still limited. No paper has been formally published testing and evaluating JEDI with FV3‐LAM according to our knowledge.…”

Section: Introductionmentioning

confidence: 99%

Implementation and Testing of Radar Data Assimilation Capabilities Within the Joint Effort for Data Assimilation Integration Framework With Ensemble Transformation Kalman Filter Coupled With FV3‐LAM Model

Park

Xue

Liu

2023

Geophysical Research Letters

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Capabilities to directly assimilate radar data are implemented within the local ensemble transform Kalman filter (LETKF) and the gain‐form LETKF (LGETKF) algorithms of the Joint Effort for Data assimilation Integration (JEDI) system. The capabilities are evaluated for the analysis and forecast of a severe convection case of 20 May 2019 in the Southern Great Plains using the limited area model version of the FV3 dynamical core (FV3‐LAM) from a recent release for Short‐Range Weather Application (SRW App). The LETKF and LGETKF implementations are shown to produce analyses and short‐range forecasts comparable to those using the ensemble square‐root Kalman Filter (EnSRF) within the Gridpoint Statistical Interpolation (GSI) framework used by current NCEP operational models. In addition, LGETKF retaining only 60% variances for model‐space vertical localization performs similarly to LGETKF retaining 99% of variance and LETKF using observation error‐based vertical localization. JEDI LETKF shows better parallel scalability than LGETKF and GSI EnSRF.

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“…The National Centers for Environmental Prediction (NCEP) is currently developing the Rapid Refresh Forecast System (RRFS; Carley et al., 2021) as part of an effort to unify the NCEP operational forecast modeling systems using the finite volume cubed sphere (FV3; Putman & Lin, 2007) dynamical core. The RRFS is planned to run at a convection‐allowing grid spacing of 3 km using the FV3 limited area model (LAM; Black et al., 2021). The RRFS will replace current NCEP operational convection‐allowing model (CAM) systems that are based on the Weather Research and Forecasting (WRF; Skamarock et al., 2008) and Nonhydrostatic Multiscale Model on the B‐grid (Janjić & Gall, 2012; Janjić et al., 2001) dynamic cores which have been extensively evaluated, developed and tuned as a CAM over the years (e.g., Aligo et al., 2018; Clark et al., 2015; Grasso et al., 2014; Johnson & Wang, 2017, 2019; Johnson, Wang, Xue, & Kong, 2011; Kain et al., 2008; Weisman et al., 2008).…”

Section: Introductionmentioning

confidence: 99%

“…at a convection-allowing grid spacing of 3 km using the FV3 limited area model (LAM; Black et al, 2021). The RRFS will replace current NCEP operational convection-allowing model (CAM) systems that are based on the Weather Research and Forecasting (WRF; Skamarock et al, 2008) and Nonhydrostatic Multiscale Model on the B-grid (Janjić & Gall, 2012;Janjić et al, 2001) dynamic cores which have been extensively evaluated, developed and tuned as a CAM over the years (e.g., Aligo et al, 2018;Clark et al, 2015;Grasso et al, 2014;Johnson & Wang, 2017, 2019Kain et al, 2008;Weisman et al, 2008).…”

mentioning

confidence: 99%

Verification and Model Configuration Sensitivity of Simulated ABI Radiance Forecasts With the FV3‐LAM Model

Johnson

Wang

Blake

et al. 2023

Earth and Space Science

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This study evaluates simulated radiance forecasts from a series of controlled experiments consisting of FV3‐LAM forecasts with different configurations of model physics and vertical resolution. The forecasts were produced during the 2020 Hazardous Weather Testbed Spring Forecasting Experiments on the same forecast cases. The evaluation includes grid‐point, neighborhood‐based and object‐based verification. The experiments include forecasts that were identical except for the physics (EMC‐LAM vs. EMC‐LAMx), vertical resolution (EMC‐LAMx vs. NSSL‐LAM), or combined initial conditions, physics and vertical resolution (GSL‐LAM). It is found that the EMC‐LAM generally provided better simulated radiance forecasts than the other three configurations at most forecast lead times, due to its unique physics configuration. All configurations generally over‐forecasted high level clouds. EMC‐LAM reduced the over‐forecasting of high clouds, but also under‐forecasted the coverage of mid‐level clouds. In contrast, at early lead times the EMC‐LAM had relatively poor performance relative to the other forecasts. Furthermore, EMC‐LAM was an outlier in terms of the vertical structure of clouds. It is also found that the NSSL‐LAM consistently improved upon the EMC‐LAMx, which had fewer vertical levels than NSSL‐LAM. Compared to EMC‐LAMx, NSSL‐LAM had less cloud over‐forecasting bias, especially with small cloud objects, and less overall error. The differences between EMC‐LAMx and GSL‐LAM were generally much smaller than the differences between EMC‐LAMx and EMC‐LAM/NSSL‐LAM. Finally, it is found that a non‐linear bias correction conditioned on symmetric brightness temperature reduced the overall root‐mean‐square error by about a factor of 2 while improving the unrealistic vertical structure of clouds in the EMC‐LAM.

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A Limited Area Modeling Capability for the Finite‐Volume Cubed‐Sphere (FV3) Dynamical Core and Comparison With a Global Two‐Way Nest

Cited by 18 publications

References 45 publications

Assimilation of FY-3D and FY-3E Hyperspectral Infrared Atmospheric Sounding Observation and Its Impact on Numerical Weather Prediction during Spring Season over the Continental United States

Assimilation of FY-3D and FY-3E Hyperspectral Infrared Atmospheric Sounding Observation and Its Impact on Numerical Weather Prediction during Spring Season over the Continental United States

Implementation and Testing of Radar Data Assimilation Capabilities Within the Joint Effort for Data Assimilation Integration Framework With Ensemble Transformation Kalman Filter Coupled With FV3‐LAM Model

Verification and Model Configuration Sensitivity of Simulated ABI Radiance Forecasts With the FV3‐LAM Model

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