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David V. Ledvina and James Pfaendtner


At this time, most current data assimilation systems use dewpoint depression data, converted to an appropriate moisture variable (relative humidity or mixing ratio), provided by rawinsondes as the lone source of moisture information. Because of the poor spatial and temporal characteristics of this data, additional moisture data are necessary to better resolve the global moisture field. This study investigates the impact of using the Special Sensor Microwave/Imager (SSM/I) total precipitable water (TPW) estimates as an additional source of moisture information.

One forecast and four data assimilation experiments were performed to determine the impact of assimilating SSM/I TPW estimates into the NASA/Goddard Earth Observing System (version 1 ) Data Assimilation System (GEOS-1 DAS). It is shown that assimilation of SSM/I TPW estimates improves the precipitation pattern in the Tropics. In addition, a known dry bias in the GEOS-1 DAS was reduced by over 50% and observation minus first guess (OF) error variance is reduced by nearly 50% after only 3 days of assimilation. Improvements were also noted in monthly and 6-h-averaged precipitation patterns when compared to other independent estimates.

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G. David Alexander, George S. Young, and David V. Ledvina


Rotated Principal component analysis (PCA) is applied to the combined vertical profiles of apparent heat source Q 1 and apparent moisture sink Q 2 from both disturbed and undisturbed periods of the Australian summer monsoon season. The data represent the heating and drying within two radiosonde arrays afforded by the Australian Monsoon Experiment (AMEX), The aim here is to identify dominant modes of variability in combined vertical profiles of Q 1 and Q 2. Rotation of the principal components (PCs)-done to assure stable, physically meaningful components-yields several PCs, deemed here to be statistically significant. The variation of individual Q 1 and Q 2 profiles from the mean profile can be expressed as linear combinations of the PCs; therefore, determination of the relative importance of each PC (through examination of its score) during differing convective conditions provides insight into their physical meaning. For instance, the contribution of PC 1 (that mode of variability that explains the maximum amount of variance between the profiles) is largest when mature cloud-cluster coverage is most expansive. Therefore, this PC is attributable to that combination of deep convection and associated stratiform anvil typical of mature cloud clusters. The remaining PCs WI into two categories: those whose contributions vary with the evolution of a convective system and those whose contributions vary diurnally. Principal components of the former group represent the effects of convection from shallow cumulus to stratiform anvil precipitation. Principal components of the latter group, those that show heating and drying patterns confined to the extremities of the troposphere, are attributable to diabatic boundary-layer fluxes and radiative processes at the top of the troposphere.

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Arthur Y. Hou, David V. Ledvina, Arlindo M. da Silva, Sara Q. Zhang, Joanna Joiner, Robert M. Atlas, George J. Huffman, and Christian D. Kummerow


This article describes a variational framework for assimilating the SSM/I-derived surface rain rate and total precipitable water (TPW) and examines their impact on the analysis produced by the Goddard Earth Observing System (GEOS) Data Assimilation System (DAS). The SSM/I observations consist of tropical rain rates retrieved using the Goddard Profiling Algorithm and tropical TPW estimates produced by Wentz.

In a series of assimilation experiments for December 1992, results show that the SSM/I-derived rain rate, despite current uncertainty in its intensity, is better than the model-generated precipitation. Assimilating rainfall data improves cloud distributions and the cloudy-sky radiation, while assimilating TPW data reduces a moisture bias in the lower troposphere to improve the clear-sky radiation. Together, the two data types reduce the monthly mean spatial bias by 46% and the error standard deviation by 26% in the outgoing longwave radiation (OLR) averaged over the Tropics, as compared with the NOAA OLR observation product. The improved cloud distribution, in turn, improves the solar radiation at the surface. There is also evidence that the latent heating change associated with the improved precipitation improves the large-scale circulation in the Tropics. This is inferred from a comparison of the clear-sky brightness temperatures for TIROS Operational Vertical Sounder channel 12 computed from the GEOS analyses with the observed values, suggesting that rainfall assimilation reduces a prevailing moist bias in the upper-tropospheric humidity in the GEOS system through enhanced subsidence between the major convective centers.

This work shows that assimilation of satellite-derived precipitation and TPW can reduce state-dependent systematic errors in the OLR, clouds, surface radiation, and the large-scale circulation in the assimilated dataset. The improved analysis also leads to better short-range forecasts, but the impact is modest compared with improvements in the time-averaged signals in the analysis. The study shows that, in the presence of biases and other errors of the forecast model, it is possible to improve the time-averaged “climate content” in the data without comparable improvements in forecast. The full impact of these data types on the analysis cannot be measured solely in terms of forecast skills.

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