CICE-LETKF Ensemble Analysis System with Application to Arctic Sea Ice Initialization

To study the effectiveness of methods to reduce errors for Arctic Sea ice initialization due to underestimation of background error covariance, an advanced ensemble analysis system has been developed. The system integrates the local ensemble transform Kalman filter (LETKF) with the community ice code (CICE). With a mixed layer ocean model used to compute the sea surface temperature (SST), the experiments on assimilation of observations of sea ice concentration (SIC) have been carried out. Assimilation experiments were performed over a 3-month period from January to March in 1997. The model was sequentially constrained with daily observation data. The effects of observation density, amplification factor for analysis error covariance, and relaxation of disturbance and spread on the results of SIC initialization were studied. It is shown that doubling the density of observation of SIC does not bring significant further improvement on the analysis result; when the ensemble size is doubled, most severe SIC biases in the Labrador, Greenland, Norwegian, and Barents seas are reduced; amplifying the analysis error covariance, relaxing disturbance, and relaxing spread all contribute to improving the reproduction of SIC with amplifying covariance with the largest magnitude.

How to improve infectious disease prediction by integrating environmental data: an application of a novel ensemble analysis strategy to predict HFMD

This study proposed a novel ensemble analysis strategy to improve hand, foot and mouth disease (HFMD) prediction by integrating environmental data. The approach began by establishing a vector autoregressive model (VAR). Then, a dynamic Bayesian networks (DBN) model was used for variable selection of environmental factors. Finally, a VAR model with constraints (CVAR) was established for predicting the incidence of HFMD in Chengdu city from 2011 to 2017. DBN showed that temperature was related to HFMD at lags 1 and 2. Humidity, wind speed, sunshine, PM10, SO2 and NO2 were related to HFMD at lag 2. Compared with the autoregressive integrated moving average model with external variables (ARIMAX), the CVAR model had a higher coefficient of determination (R2, average difference: + 2.11%; t = 6.2051, P = 0.0003 < 0.05), a lower root mean-squared error (−24.88%; t = −5.2898, P = 0.0007 < 0.05) and a lower mean absolute percentage error (−16.69%; t = −4.3647, P = 0.0024 < 0.05). The accuracy of predicting the time-series shape was 88.16% for the CVAR model and 86.41% for ARIMAX. The CVAR model performed better in terms of variable selection, model interpretation and prediction. Therefore, it could be used by health authorities to identify potential HFMD outbreaks and develop disease control measures.