In atmospheric data assimilation systems, the forecast error covariance model is an important component. However, the paralneters required by a forecast error covariance model are difficult to obtain due to the absenc...In atmospheric data assimilation systems, the forecast error covariance model is an important component. However, the paralneters required by a forecast error covariance model are difficult to obtain due to the absence of the truth. This study applies an error statistics estimation method to the Pfiysical-space Statistical Analysis System （PSAS） height-wind forecast error covariance model. This method consists of two components： the first component computes the error statistics by using the National Meteorological Center （NMC） method, which is a lagged-forecast difference approach, within the framework of the PSAS height-wind forecast error covariance model; the second obtains a calibration formula to rescale the error standard deviations provided by the NMC method. The calibration is against the error statistics estimated by using a maximum-likelihood estimation （MLE） with rawindsonde height observed-minus-forecast residuals. A complete set of formulas for estimating the error statistics and for the calibration is applied to a one-month-long dataset generated by a general circulation model of the Global Model and Assimilation Office （GMAO）, NASA. There is a clear constant relationship between the error statistics estimates of the NMC-method and MLE. The final product provides a full set of 6-hour error statistics required by the PSAS height-wind forecast error covariance model over the globe. The features of these error statistics are examined and discussed.展开更多
The synoptic climatology of monthly mean surface air temperature anomaly (MMATA) at 47 surface stations in Asia is assessed by two types of maps. The first consists of simple linear correlation coefficients between th...The synoptic climatology of monthly mean surface air temperature anomaly (MMATA) at 47 surface stations in Asia is assessed by two types of maps. The first consists of simple linear correlation coefficients between the MMATA at a station and the monthly mean 700 hPa height anomaly (MMHA) at 134 grid points in the Northern Hemisphere. In winter, these fields generally contain two equally important centers: a center of maximum positive correlation approximately 1300 km from the reference station and a negative center about 2800 km to its northwest. In summer, the positive center is located an average of 800 km from the reference station and strongly dominates the MMATA, while the negative center is no longer as important as in winter. The second kind of map is a special type of anomaly composite, constructed by multiplying the correlation coefficient at each grid point by the standard deviation of MMHA at the same point. This map shows the optimum distribution of height anomalies for abnormally warm (or cold) weather for winter and summer in each of several climatologically similar regions into which Asia is subdivided.展开更多
文摘In atmospheric data assimilation systems, the forecast error covariance model is an important component. However, the paralneters required by a forecast error covariance model are difficult to obtain due to the absence of the truth. This study applies an error statistics estimation method to the Pfiysical-space Statistical Analysis System （PSAS） height-wind forecast error covariance model. This method consists of two components： the first component computes the error statistics by using the National Meteorological Center （NMC） method, which is a lagged-forecast difference approach, within the framework of the PSAS height-wind forecast error covariance model; the second obtains a calibration formula to rescale the error standard deviations provided by the NMC method. The calibration is against the error statistics estimated by using a maximum-likelihood estimation （MLE） with rawindsonde height observed-minus-forecast residuals. A complete set of formulas for estimating the error statistics and for the calibration is applied to a one-month-long dataset generated by a general circulation model of the Global Model and Assimilation Office （GMAO）, NASA. There is a clear constant relationship between the error statistics estimates of the NMC-method and MLE. The final product provides a full set of 6-hour error statistics required by the PSAS height-wind forecast error covariance model over the globe. The features of these error statistics are examined and discussed.
文摘The synoptic climatology of monthly mean surface air temperature anomaly (MMATA) at 47 surface stations in Asia is assessed by two types of maps. The first consists of simple linear correlation coefficients between the MMATA at a station and the monthly mean 700 hPa height anomaly (MMHA) at 134 grid points in the Northern Hemisphere. In winter, these fields generally contain two equally important centers: a center of maximum positive correlation approximately 1300 km from the reference station and a negative center about 2800 km to its northwest. In summer, the positive center is located an average of 800 km from the reference station and strongly dominates the MMATA, while the negative center is no longer as important as in winter. The second kind of map is a special type of anomaly composite, constructed by multiplying the correlation coefficient at each grid point by the standard deviation of MMHA at the same point. This map shows the optimum distribution of height anomalies for abnormally warm (or cold) weather for winter and summer in each of several climatologically similar regions into which Asia is subdivided.
