Real-Time Four-Dimensional Data Assimilation (RTFDDA) and Forecasting Advances

BACKGROUND

RTFDDA is a mesoscale numerical weather modeling technology that has been developed to meet the critical weather needs, such as military tests and operations, renewable energy assessment and prediction, power grid weather safety, weather-related emergency response etc.  RTFDDA is an extension of the WRF (Weather Research and Forecasting) model and is designed to effectively and efficiently assimilate diverse available weather observations into WRF and provide rapid-update precision weather information for local areas of interests. An important feature of RTFDDA is that it allows for smooth and uninterrupted assimilation of diverse weather observations and produces physically consistent and dynamically balanced 4D weather analyses and “cloud/precipitation “spun-up” predictions. In the past 18 years, RTFDDA has been applied to over 50 weather-critical applications across the US and the international regions.  

Technologies

Figure 1. RTFDDA and its extension for regional microclimatology dynamical downscaling, ensemble prediction and LES/VLES NWP of microscale flows.
Figure 1. RTFDDA and its extension for regional microclimatology dynamical downscaling, ensemble prediction and LES/VLES NWP of microscale flows.

RTFDDA has been continuously improved with respect to its data assimilation schemes, new data source, dynamical configuration and physical parameterizations to advance the RTFDDA system itself and improve its accuracy and capabilities. In the last 10 years, RTFDDA has evolved from a single mesoscale deterministic analysis and forecasting system to a modeling capability suite that integrates ensemble prediction technology (Ensemble-RTFDDA), regional climate downscaling with four-dimensional data assimilation (Climate-FDDA) and microscale NWP with a refined LES model grid at 10s to 100s meters of grid intervals (RTFDDA-LES) (Fig. 1). As a result, RTFDDA is now capable of producing customized high-resolution and ultra-high-resolution precision weather analysis and forecasts, probabilistic weather forecasts, and multi-year/multi-decadal microclimatology analyses for a given target region. In addition, the WRF-Chem model has been added to RTFDDA for forecasting air quality and sand and dust storms.     

RTFDDA integrates the following data assimilation technologies: Newtonian relaxation based “observation-nudging” and “analysis-nudging” FDDA schemes, the NCAR community WRFDA, GSI, DART-EnKF data assimilation modules, a four-dimensional relaxation ensemble Kalman filter (4D-REKF) FDDA scheme and a hydrometeor-latent-heat-nudging (HLHN) radar data assimilation schemes. The technologies are configured to formulate hybrid data assimilation to provide optimal modeling according to the application requirements.  

FY 2018 ACCOMPLISHMENTS 

Radar Data Assimilation  

Weather radars detects detailed hydrometeor information inside convection systems. Radar data assimilation (RDA) has been an important measure to improve mesoscale numerical predictions of precipitation systems and the accompanying severe weather phenomena. A significant effort has been put into refining the RTFDDA-RDA scheme. Studies have been carried out to assess the impact of two key experimental parameters, the time windows and weights and the nudging coefficients, in the hydrometeor and latent heat nudging (HLHN) scheme, the core engine of RTFDDA RDA. This research greatly improved our understanding of the complex digesting processes of the RDA innovations and led to a new design of different latent heat controls for stratiform and convective clouds. This work significantly improves the RTFDDA-RDA performance for 0 – 6 hr convection forecast.

Lightning Data Assimilation  

Figure 2. Verification of RTFDDA-LDA forecasts of a squall line event. Left panels: observed total lightning density; Middle: RTFDDA 0, 1, and 3 hour forecasts without LDA; and Right: RTFDDA-LDA 0, 1, and 3 hour forecasts. The red lines in the middle and right panels shows the areas of observed lightning.
Figure 2. Verification of RTFDDA-LDA forecasts of a squall line event. Left panels: observed total lightning density; Middle: RTFDDA 0, 1, and 3 hour forecasts without LDA; and Right: RTFDDA-LDA 0, 1, and 3 hour forecasts. The red lines in the middle and right panels shows the areas of observed lightning.

Another major advance with RTFDDA is a development of a lightning data assimilation (LDA) capability. Total lightning strikes are highly correlated with the total graupel mass in convection clouds. An algorithm was developed to reconstruct graupel amount (specific mixing ratios) for three-dimensional model grid points based on total lightning observations. The reconstructed graupel was then assimilated into WRF based on the RTFDDA HLHN scheme and also an EnKF-based time-lagged ensemble approach. Case studies with severe convection events occurred in both the central plains, US and a southern China region demonstrated that RTFDDA-LDA is capable of significantly improving 0 – 3 hr lightning and precipitation forecast of convective storms. This work has been published on the Journal of Geophysics Research: Atmosphere (Wang et al. 2018).     

Sand and dust forecasting with WRF-Chem

Dust forecast capabilities have been developed with RTFDDA based on the fully coupled WRF-Chem. RTFDDA data assimilation of weather data is able to provide accurate weather environment for modeling dust emission and transportation in WRF-Chem and effectively improves the simulation of dust spin-up processes and thus the forecast accuracy. RTFDDA-dust forecasting systems have been deployed for the Mid-East countries including Saudi Arabia and Israel. 

PLANS FOR FY 2019

Research and development efforts will be carried out to further improve the RTFDDA key components. In particular, the new community WRF research achievements, and the nudging-based FDDA scheme shall be merged and integrated for new deployments. All operational RTFDDA systems will be upgraded to WRF Version 4.0. A plan has been set up to develop a RTFDDA framework based on the MPAS model.      

The RTFDDA data assimilation scheme will be enhanced by integrating the community advances in GSI and DART, improving its own “nudging”, HLHN and EnKF based radar and lightning data assimilation system, and expanding the four-dimensional relaxation ensemble Kalman filter (4D-REKF) four-dimensional data assimilation scheme. 4D-REKF is an advanced FDDA capability that combines the advantages of Newtonian relaxation based “observation nudging” and the advanced ensemble Kalman filter. 4D-REKF using flow-dependent data assimilation weights generated with dynamical ensemble forecasts and historical forecast analogs will also be studied. For radar and lightning data assimilation, GSI and 4D-REKF data assimilation schemes will be assessed and compared for assimilation of Doppler radar radial velocities and polarimetric radar products. In addition, algorithms for jointly assimilating polarimetric radar data and lightning measurements will be studied.