Ensemble Kalman Filter Data Assimilation for the Model for Prediction Across Scales (MPAS)

A global atmospheric analysis and forecast system is constructed based on the atmospheric component of the Model for Prediction Across Scales (MPAS-A) and the Data Assimilation Research Testbed (DART) ensemble Kalman filter. The system is constructed using the unstructured MPAS-A Voronoi (nominally hexagonal) mesh and thus facilitates multiscale analysis and forecasting without the need for developing new covariance models at different scales. Cycling experiments with the assimilation of real observations show that the global ensemble system is robust and reliable throughout a one-month period for both quasi-uniform and variable-resolution meshes. The variable-mesh assimilation system consistently provides higher-quality analyses than those from the coarse uniform mesh, in addition to the benefits of the higher-resolution forecasts, which leads to substantial improvements in 5-day forecasts. Using the fractions skill score, the spatial scale for skillful precipitation forecasts is evaluated over the high-resolution area of the variable-resolution mesh. Skill decreases more rapidly at smaller scales, but the variable mesh consistently outperforms the coarse uniform mesh in precipitation forecasts at all times and thresholds. Use of incremental analysis updates (IAU) greatly decreases high-frequency noise overall and improves the quality of EnKF analyses, particularly in the tropics. Important aspects of the system design related to the unstructured Voronoi mesh are also investigated, including algorithms for handling the C-grid staggered horizontal velocities.

Download Full-text

Scale-Dependent Representation of the Information Content of Observations in the Global Ensemble Kalman Filter Data Assimilation

Monthly Weather Review ◽

10.1175/mwr-d-15-0401.1 ◽

2016 ◽

Vol 144 (8) ◽

pp. 2927-2945

Author(s):

Nedjeljka Žagar ◽

Jeffrey Anderson ◽

Nancy Collins ◽

Timothy Hoar ◽

Kevin Raeder ◽

...

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Information Content ◽

Ensemble Kalman Filter ◽

Large Scale ◽

Variance Reduction ◽

Weather Prediction ◽

Current Data ◽

Model Framework ◽

The Tropics

Abstract Global data assimilation systems for numerical weather prediction (NWP) are characterized by significant uncertainties in tropical analysis fields. Furthermore, the largest spread of global ensemble forecasts in the short range on all scales is in the tropics. The presented results suggest that these properties hold even in the perfect-model framework and the ensemble Kalman filter data assimilation with a globally homogeneous network of wind and temperature profiles. The reasons for this are discussed by using the modal analysis, which provides information about the scale dependency of analysis and forecast uncertainties and information about the efficiency of data assimilation to reduce the prior uncertainties in the balanced and inertio-gravity dynamics. The scale-dependent representation of variance reduction of the prior ensemble by the data assimilation shows that the peak efficiency of data assimilation is on the synoptic scales in the midlatitudes that are associated with quasigeostrophic dynamics. In contrast, the variance associated with the inertia–gravity modes is less successfully reduced on all scales. A smaller information content of observations on planetary scales with respect to the synoptic scales is discussed in relation to the large-scale tropical uncertainties that current data assimilation methodologies do not address successfully. In addition, it is shown that a smaller reduction of the large-scale uncertainties in the prior state for NWP in the tropics than in the midlatitudes is influenced by the applied radius for the covariance localization.

Download Full-text

Predictability and Genesis of Hurricane Karl (2010) Examined through the EnKF Assimilation of Field Observations Collected during PREDICT

Journal of the Atmospheric Sciences ◽

10.1175/jas-d-13-0291.1 ◽

2014 ◽

Vol 71 (4) ◽

pp. 1260-1275 ◽

Cited By ~ 8

Author(s):

Jonathan Poterjoy ◽

Fuqing Zhang

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Ensemble Kalman Filter ◽

Control Experiment ◽

Initial Condition ◽

Field Observations ◽

Ensemble Forecasts ◽

Thermodynamic Structure ◽

Cloud Systems ◽

The Tropics

Abstract The genesis of Hurricane Karl (2010) is explored using analyses and forecasts from a cycling ensemble Kalman filter (EnKF) that assimilates routinely collected observations as well as dropsonde measurements that were taken during the Pre-Depression Investigation of Cloud Systems in the Tropics (PREDICT) field campaign. A total of 127 dropsonde observations were collected from six PREDICT flight missions over a 5-day period before and during Karl’s genesis. EnKF analyses that take into account the PREDICT dropsondes provide a detailed four-dimensional overview of the evolving kinematic and thermodynamic structure within the pregenesis disturbance. In particular, the additional field observations are found to increase the low- and midlevel circulation and column moisture in the EnKF analyses and reduce the position error of the low-level vortex. Deterministic forecasts from these analyses show a 24-h improvement in predicting genesis over a control experiment that omits the dropsonde observations. In ensemble forecasts for this event, the more accurate analyses translate into a higher confidence in predicting the intensification of Karl; that is, data assimilation experiments also suggest that initial condition errors at the mesoscale pose large challenges for predicting genesis, thus highlighting the need for improved observation networks and more advanced data assimilation methods.

Download Full-text

Data assimilation with the weighted ensemble Kalman filter

Tellus A Dynamic Meteorology and Oceanography ◽

10.3402/tellusa.v62i5.15716 ◽

2010 ◽

Author(s):

Nicolas Papadakis ◽

Etienne MÃ©min ◽

Anne Cuzol ◽

Nicolas Gengembre

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Ensemble Kalman Filter

Download Full-text

Data assimilation with the ensemble Kalman filter in a numerical model of the North Sea

Ocean Dynamics ◽

10.1007/s10236-016-0968-5 ◽

2016 ◽

Vol 66 (8) ◽

pp. 955-971 ◽

Cited By ~ 3

Author(s):

Stéphanie Ponsar ◽

Patrick Luyten ◽

Valérie Dulière

Keyword(s):

Kalman Filter ◽

Numerical Model ◽

Data Assimilation ◽

North Sea ◽

Ensemble Kalman Filter ◽

The North ◽

The North Sea

Download Full-text

Convection-Permitting Forecasts Initialized with Continuously Cycling Limited-Area 3DVAR, Ensemble Kalman Filter, and “Hybrid” Variational–Ensemble Data Assimilation Systems

Monthly Weather Review ◽

10.1175/mwr-d-13-00100.1 ◽

2014 ◽

Vol 142 (2) ◽

pp. 716-738 ◽

Cited By ~ 49

Author(s):

Craig S. Schwartz ◽

Zhiquan Liu

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Three Dimensional ◽

Skill Score ◽

Limited Area ◽

Computational Domain ◽

Ensemble Data Assimilation ◽

Ensemble Data ◽

Precipitation Characteristics ◽

High Precipitation

Abstract Analyses with 20-km horizontal grid spacing were produced from parallel continuously cycling three-dimensional variational (3DVAR), ensemble square root Kalman filter (EnSRF), and “hybrid” variational–ensemble data assimilation (DA) systems between 0000 UTC 6 May and 0000 UTC 21 June 2011 over a domain spanning the contiguous United States. Beginning 9 May, the 0000 UTC analyses initialized 36-h Weather Research and Forecasting Model (WRF) forecasts containing a large convection-permitting 4-km nest. These 4-km 3DVAR-, EnSRF-, and hybrid-initialized forecasts were compared to benchmark WRF forecasts initialized by interpolating 0000 UTC Global Forecast System (GFS) analyses onto the computational domain. While important differences regarding mean state characteristics of the 20-km DA systems were noted, verification efforts focused on the 4-km precipitation forecasts. The 3DVAR-, hybrid-, and EnSRF-initialized 4-km precipitation forecasts performed similarly regarding general precipitation characteristics, such as timing of the diurnal cycle, and all three forecast sets had high precipitation biases at heavier rainfall rates. However, meaningful differences emerged regarding precipitation placement as quantified by the fractions skill score. For most forecast hours, the hybrid-initialized 4-km precipitation forecasts were better than the EnSRF-, 3DVAR-, and GFS-initialized forecasts, and the improvement was often statistically significant at the 95th percentile. These results demonstrate the potential of limited-area continuously cycling hybrid DA configurations and suggest additional hybrid development is warranted.

Download Full-text