Abstract
The key technical features of RMAPS-STv2.0, the Hourly Rapid Catch-up Cycling Assimilation and Forecast System, are introduced in detail. The initialization approach used by the system, known as incremental analysis update (IAU), successfully suppresses the initial noise accumulation issue. The two coupling components, such as cycle analysis and forecast updates with the implementations of data with different cut-off times, run in turn within each hourly cycling to meet the high demands raised by the nowcasting and short-term forecast service by taking into full consideration the actual truncated time of all kinds of observations’ arrival. The dynamic constraint of the global model’s large-scale field on the growth of the regional model’s small- and medium-scale thermal dynamic field is realized through the application of the dynamic forecast hybrid scheme to the assimilated background field, and the deformation of the large-scale prediction field brought on by the accumulation of the rapid update cycle prediction errors is effectively suppressed. To prevent the continual accumulation of water vapor, the national-wide mosaic radar reflectivity is only assimilated during the forecast update stage. The optimization of the radar assimilation background field error variance and length scale technique successfully encouraged the use of the radar assimilation effect. Additionally, the application of national wind profile radar observation data in real time is accomplished. A series of optimization of physical parameterization schemes have been performed. The cloud radiative forcing scheme, planetary boundary layer, and surface-layer scheme have all been optimized to address the systematic bias in diurnal 2m temperature and humidity. The updates of vegetation coverage and soil type with Noah’s new soil hydraulics parameter table also contribute to the better balance of the surface energy budget and the energy transfer between the ground and the atmosphere in the model. Additionally, a scale-aware cumulus convection parameterization scheme is implemented to the system to enhance the precipitation forecast performance of the cumulus scheme and reduce overprediction errors for light precipitation.
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Abbreviations
- Name:
-
Description
- 3DVAR:
-
Three-dimensional Variational DA
- ACM2:
-
Asymmetrical Convective Model version 2
- ARW:
-
Advanced Research Model
- BNU:
-
Beijing Normal University
- BST:
-
Beijing Standard Time
- CMA:
-
China Meteorology Administration
- CPS:
-
Cumulus Parameterization Scheme
- DB:
-
Dynamic Blending
- ECMWF:
-
European Center for Medium-Range Weather Forecasts
- ETS:
-
Equitable Skill Score
- FAO:
-
The Food and Agriculture Organization
- GNSS:
-
Global Navigation Satellite System
- GTS:
-
Global Telecommunication System
- IAU:
-
Incremental Analysis Updates
- IRMCD:
-
Iterated Reweighted Minimum Covariance Determinant
- IUM:
-
Institute of Urban Meteorology
- LHF:
-
Latent Heating Flux
- LSM:
-
Land Surface Model
- MODIS:
-
Moderate Resolution Imaging Spectroradiometer
- Noah:
-
NOAA/NCEP–Oregon State University–Air Force Research Laboratory–NOAA/Office of Hydrology land surface model
- OMB:
-
Observation-background deviation (OMB)
- PBL:
-
Planetary Boundary Layer
- RMAPS:
-
Rapid Refresh Multiscale Analysis and Prediction System
- RMSE:
-
Root Mean Square Error
- RRTMG:
-
Rapid Radiative Transfer Model for General circulation model applications
- SHF:
-
Sensible Heating Flux
- SL:
-
Surface Layer
- TS:
-
Threat Score
- WRF:
-
Weather Research and Forecasting Model
- WRFDA:
-
WRF model’s Community Variational/Ensemble Data Assimilation System
- YSU:
-
Yonsei University PBL Scheme
- ZTD:
-
Zenith Total Delay
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Chen, M. et al. (2023). Development of the RMAPS-STv2.0 Hourly Rapid Updated Catch-up Cycling Assimilation and Forecast System. In: Park, S.K. (eds) Numerical Weather Prediction: East Asian Perspectives. Springer Atmospheric Sciences. Springer, Cham. https://doi.org/10.1007/978-3-031-40567-9_3
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