Guocan Wu

Mapping near-surface air temperature, pressure, relative humidity and wind speed over Mainland China with high spatiotemporal resolution

As part of a joint effort to construct an atmospheric forcing dataset for mainland China with high spatiotemporal resolution, a new approach is proposed to construct gridded near-surface temperature, relative humidity, wind speed and surface pressure with a resolution of 1 km×1 km. The approach comprises two steps: (1) fit a partial thin-plate smoothing spline with orography and reanalysis data as explanatory variables to ground-based observations for estimating a trend surface; (2) apply a simple kriging procedure to the residual for trend surface correction.The proposed approach is applied to observations collected at approximately 700 stations over mainland China. The generated forcing fields are compared with the corresponding components of the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis dataset and the Princeton meteorological forcing dataset. The comparison shows that, both within the station network and within the resolutions of the two gridded datasets, the interpolation errors of the proposed approach are markedly smaller than the two gridded datasets.

Using Analysis State to Construct a Forecast Error Covariance Matrix in Ensemble Kalman Filter Assimilation

Correctly estimating the forecast error covariance matrix is a key step in any data assimilation scheme. If it is not correctly estimated, the assimilated states could be far from the true states. A popular method to address this problem is error covariance matrix inflation. That is, to multiply the forecast error covariance matrix by an appropriate factor. In this paper, analysis states are used to construct the forecast error covariance matrix and an adaptive estimation procedure associated with the error covariance matrix inflation technique is developed.The proposed assimilation scheme was tested on the Lorenz-96 model and 2D Shallow Water Equation model, both of which are associated with spatially correlated observational systems. The experiments showed that by introducing the proposed structure of the forecast error covariance matrix and applying its adaptive estimation procedure, the assimilation results were further improved.

Inflation adjustment on error covariance matrix of ensemble Kalman filter

In ensemble Kalman filter assimilation, the estimated forecast error covariance matrix and prior observational error covariance matrix could be far from the truth. This is likely to significantly affect the assimilation results. To compensate, this paper introduce two inflation factors to adjust forecast and observational error covariance respectively and estimate them simultaneously in one assimilation circle. The proposed schemes are tested using Lorenz-96 model, with a class of nonlinear observational operators. It illustrates that the improved assimilation schemes perform better than the original scheme.

Chinese regional high space-time resolution fusion rainfall model exploration based on site and remote sensing data

This paper used the methods of thin-plate spline multisource data fusion, generating China area precipitation for 3 hours time resolution and spatial resolution of 5×5 km. The comparison with general interpolation, Kriging interpolation and remote sensing data inversion method through the actual data illustrates the superiority of the proposed method.

Prediction method of the transformed data

In Meteorology, we often get data products we need by doing statistical analysis to observation data. Its very important for meteorology forecasting and disaster Warning. When we do statistic modeling, the data are not always normal distribution, so we transform the data to a new one first and then modeling. In this paper, we mainly consider how to forecast origin variable from experience model forecast value. On this question, people always consider less about the error from inverse transformation. We mainly use Monte Carlo method, which can be used to deal with kinds of transform function. In the third part, I have simulated logarithmic transformation to compare RMSE of Monte Carlo method and inverse. It can be see that Monte Carlo can decrease origin variables prediction error.