Evaluating Forecast Performance and Sensitivity to the GSI EnKF Data Assimilation Configuration for the 28 – 29 May 2017 Mesoscale Convective System Case

In an effort to improve radar data assimilation configurations for potential operational implementation, GSI EnKF data assimilation experiments based on the operational system employed by the Center for Analysis and Prediction of Storms (CAPS) realtime Spring Forecast Experiments are performed. These experiments are followed by 6-hour forecasts for an MCS on 28 – 29 May 2017. Configurations examined include data thinning, covariance localization radii and inflation, observation error settings, and data assimilation frequency for radar observations.The results show experiments that assimilate radar observations more frequently (i.e., 5 – 10 minutes) are initially better at suppressing spurious convection. However, assimilating observations every 5 minutes causes spurious convection to become more widespread with time, and modestly degrades forecast skill through the remainder of the forecast window. Ensembles that assimilate more observations with less thinning of data or use a larger horizontal covariance localization radius for radar data predict fewer spurious storms and better predict the location of observed storms. Optimized data thinning and horizontal covariance localization radii have positive impacts on forecast skill during the first forecast hour that are quickly lost due to the growth of forecast error. Forecast skill is less sensitive to the ensemble spread inflation factors and observation errors tested during this study. These results provide guidance towards optimizing the configuration of the GSI EnKF system. Among DA the configurations tested, the one employed by the CAPS Spring Forecast Experiment produces the most skilled forecasts while remaining computationally efficient for realtime use.

COMPARISON AND ANALYSIS OF THINNING METHODS FOR MULTI-BEAM SOUNDING DATA

Marine surveying and mapping is the basis of all marine development activities, and underwater topographic survey is one of the essential tasks of it. The multi-beam sounding system can give dozens or even hundreds of water depth values in the vertical plane perpendicular to the course at a time, and there is a lot of redundancy in these data. Efficient compression can make better use of water depth data, improve work efficiency, save system hardware resources, and facilitate rapid mapping and the construction of submarine topography model. Thinning requires an optimal balance between data accuracy and sampling density. In this paper, several commonly used thinning methods are selected and applied to the sounding data for experiments, and the application effects of different thinning methods are analyzed and compared. The results show that the mesh-based and system-based thinning methods are simple and efficient, and the results are more evenly distributed. It works well in areas with flat topography and low complexity. But in the area with large relief, the result of thinning may not take into account the topographical features, and the effect of topography representation is poor. The thinning method based on distance and elevation difference takes the elevation factor into account and has a better performance in preserving topography features. However, this method needs to search the points in a given range constantly, and it is inefficient to apply it to large amounts of data. The thinning method based on the Douglas-Peucker algorithm only considers the spatial relationship within each ping data, and the thinning result is not reasonable enough. This paper can provide reference for sounding data thinning.

LiDAR-supported estimation of change in forest biomass with time-invariant regression models

A single a priori chosen linear regression model with two alternative error structures is proposed for model-assisted (MA) and model-dependent (MD) estimation of state and change in aboveground tree biomass (AGB, Mg·ha−1) in three forest strata in the Våler forest in southeastern Norway. Field data of tree height and stem diameter were collected in 145 permanent 200 m2circular plots. Concurrent LiDAR data were collected for the entire forest. The regression model includes two LiDAR-based explanatory variables: the mean of canopy height raised to a power of 1.5 and the standard deviation of canopy heights. A nearest-neighbour thinning of the 2010 LiDAR data to the density of the 1999 data was implemented to counter density effects in the explanatory variables. Estimates of change based on a single regression model were more accurate than estimating change from year-specific models (and no data thinning). A canopy height dependent correlated error structure was preferred over a partitioning of the error to temporary and “permanent” plot effects. For point estimates of AGB in 1999 and 2010, MA and MD estimates of errors were numerically comparable, but MD errors of change were much smaller than corresponding MA errors.

The Impacts of Representing the Correlation of Errors in Radar Data Assimilation. Part I: Experiments with Simulated Background and Observation Estimates

In radar data assimilation, statistically optimal analyses are sought by minimizing a cost function in which the variance and covariance of background and observation errors are correctly represented. Radar observations are particular in that they are often available at spatial resolution comparable to that of background estimates. Because of computational constraints and lack of information, it is impossible to perfectly represent the correlation of errors. In this study, the authors characterize the impact of such misrepresentations in an idealized framework where the spatial correlations of background and observation errors are each described by a homogeneous and isotropic exponential decay. Analyses obtained with perfect representation of correlations are compared to others obtained by neglecting correlations altogether. These two sets of analyses are examined from a theoretical and an experimental perspective. The authors show that if the spatial correlations of background and observation errors are similar, then neglecting the correlation of errors has a small impact on the quality of analyses. They suggest that the sampling noise, related to the precision with which analysis errors may be estimated, could be used as a criterion for determining when the correlations of errors may be omitted. Neglecting correlations altogether also yields better analyses than representing correlations for only one term in the cost function or through the use of data thinning. These results suggest that the computational costs of data assimilation could be reduced by neglecting the correlations of errors in areas where dense radar observations are available.