<p>The Data Assimilation Research Testbed (DART) is a community facility for ensemble data assimilation developed and maintained by the National Center for Atmospheric Research (NCAR). DART provides ensemble data assimilation capabilities for NCAR community earth system models and many other prediction models. It is straightforward to add interfaces for new models and new observations to DART.</p><p>DART provides traditional ensemble data assimilation algorithms that implicitly assume Gaussianity and linearity. Traditional algorithms can still work when these assumptions are violated. However, it is possible to greatly improve results by extending ensemble algorithms to explicitly account for aspects of nonlinearity and non-Gaussianity. Two new algorithms have been added to DART. 1). Anamorphosis transforms variables to make the assimilation problem more linear and Gaussian before transforming posterior estimates back to the original model variables; 2). The marginal correction rank histogram filter (MCRHF) directly represents arbitrary non-Gaussian distributions. These methods are particularly valuable for data assimilation for bounded quantities like tracers or streamflow.</p><p>DART is being applied to a number of novel applications. Examples in the poster include 1). An eddy-resolving global ocean ensemble reanalysis with the POP ocean model and an ensemble optimal interpolation; 2). The WRF-Hydro/DART system now includes a multi-parametric ensemble, anamorphosis, and spatially-correlated noise for the forcing fields. 3). Results from the Carbon Monitoring System over Mountains using CLM5 to assimilate remotely-sensed observations (LAI, biomass, and SIF) for a field site in Colorado; 4). Assimilation of MODIS snow cover fraction and daily GRACE total water storage data and its impact on soil moisture using the DART/NOAH-MP system. 5). An ensemble atmospheric reanalysis using the CAM general circulation model.</p>

<p>The Data Assimilation Research Testbed (DART) has been coupled with the community WRF-Hydro modeling system with the intent of providing efficient and flexible support for assimilating a wide range of streamflow and soil moisture observations and delivering an ensemble of model states useful for quantifying streamflow uncertainties. The coupled framework, named Hydro-DART, is used to study and assess the flooding consequences of Hurricane Florence over the Carolinas during August-September 2018 period.<br>Several extensions to earlier versions of Hydro-DART have been explored. These include: (1) a multi-configuration ensemble in which different ensemble members are run with different physical parameters (e.g., Manning's roughness and channel geometry) in order to create additional ensemble variability, (2) a variable transform, anamorphosis, which is introduced such that bounded quantities (e.g., streamflow) are transformed to a Gaussian space prior to the Kalman update as a way to avoid non-physical state updates, (3) a spatially-correlated noise, which is introduced to represent uncertainty of input forcings (e.g., overland and subsurface fluxes) in a physically meaningful way, and (4) an along-the-stream localization, which considers precipitation correlation length scale, rather than physical proximity. Hourly streamflow gauge data, from the flood-affected area, is used to test the impact of these extensions on the overall prediction accuracy. Analyses and hindcasts are compared to those based on the nudging assimilation currently employed in the National Water  Model (NWM) operations. Standard streamflow forecast metrics are also supplemented by a wavelet-based event timing error metric.</p>

Society's ability to make wise decisions depends onan accurate understanding of the current state of Earthand on an ability to predict future states.The Data Assimilation Research Testbed (DART) is an example of a suite of toolsdesigned to improve our understanding through the combination of observationswith our theoretical understanding embodied in forecast models.DART's ensemble based data assimilation provides uncertainty quantification as a function of time, location, and variable.Current research using DART includes: Improving streamflow prediction during intense rainfall events, which lead to flooding, using DART and the Weather Research and Forecasting model and the Noah-MP land model (WRF-Hydro). Building an integrated atmosphere and ocean forecasting system using DART and WRF for the Red Sea Initiative. Understanding air pollution using a global meteorology-aerosol-chemistry prediction system that assimilates aerosol optical depth, carbon monoxide, and weather observations into the Community Atmosphere Model with Chemistry (CAM-Chem). Assimilating observations of the Earth system from satellites into the Model for Prediction Across Scales (MPAS; regional and global) using observation operators from the Joint Effort for Data assimilation Integration (JEDI), bias correction for satellite retrievals from the Gridpoint Statistical Interpolation (GSI), and the assimilation environment of DART. Deciphering the flow dependency of forecast errors in the tropics and the relative importance of wind and mass information for tropical analyses. Connecting the U.S. Department of Energy's E3SM atmospheric model with a broad spectrum of observations to perform short ensemble hindcast simulations for model development and evaluation. Generating atmospheric reanalysis data sets from CAM, which enables efficient data assimilation in other components of the Earth system; ocean, land, cryosphere, . . . Improving DART by giving users more control over how observations are assimilated, and supporting the assimilation of additional observations, such as radiances through the use of the RTTOV software.