Tilt corrections over complex terrain and their implication for CO<Subscript>2</Subscript> transport

Observational data from sonic anemometers are commonly rotated from sonic to streamline coordinates, a procedure that is called tilt correction. Tilt corrections are often used to post-process air velocity data collected from sonic anemometers to allow objective interpretation of air flow data relative to the Earth. Since streamline coordinates depend on dynamical characteristics of the flow, the tilt correction depends not only on temporal and spatial variations of the flow, but also on local circulations. We found that ensemble- averaged slope flows are approximately parallel to the terrain slope close to the ground within the canopy layer, but not above, due to the influence of the diurnal variation of local vertical circulations. As a result, the diurnal variation of the observed vertical velocity in streamline coordinates at 21.5 m above the ground over 11-m tall forest canopies can be opposite to that calculated from the continuity equation. To estimate CO₂ transport over sloping terrain, a workable reference coordinate system is needed such that multiple sonic anemometers have a common reference relative to the Earth. Streamline coordinate systems can be the choice of the common reference coordinate system only if flow, at least ensemble-averaged flow, is parallel to terrain slopes. The choice of the reference coordinate system and its implication in investigation of CO₂ transport are discussed. The National Center for Atmospheric Research is sponsored by the National Science Foundation.     

Validation of climate model output using Bayesian statistical methods

The growing interest in and emphasis on high spatial resolution estimates of future climate has demonstrated the need to apply regional climate models (RCMs) to that problem. As a consequence, the need for validation of these models, an assessment of how well an RCM reproduces a known climate, has also grown. Validation is often performed by comparing RCM output to gridded climate datasets and/or station data. The primary disadvantage of using gridded climate datasets is that the spatial resolution is almost always different and generally coarser than climate model output. We have used a Bayesian statistical model derived from observational data to validate RCM output. We used surface air temperature (SAT) data from 109 observational stations in California, all with records of approximately 50 years in length, and created a statistical model based on this data. The statistical model takes into account the elevation of the station, distance from coastline, and the NOAA climate region in which the station resides. Analysis indicates that the statistical model provides reliable estimates of the mean monthly SAT at any given station. In our method, the uncertainty in the estimates produced by the statistical model are directly determined by obtaining probability density functions for predicted SATs. This statistical model is then used to estimate average SATs corresponding to each of the climate model grid cells. These estimates are compared to the output of the RCM to assess how well the RCM matches the observed climate as defined by the statistical model. Overall, the match between the RCM output and the statistical model is good, with some deficiencies likely due in part to the representation of topography in the RCM.     

Shear stress partitioning in large patches of roughness in the atmospheric inertial sublayer

Drag partition measurements were made in the atmospheric inertial sublayer for six roughness configurations made up of solid elements in staggered arrays of different roughness densities. The roughness was in the form of a patch within a large open area and in the shape of an equilateral triangle with 60 m long sides. Measurements were obtained of the total shear stress (τ) acting on the surfaces, the surface shear stress on the ground between the elements (τ_S) and the drag force on the elements for each roughness array. The measurements indicated that τ_S quickly reduced near the leading edge of the roughness compared with τ, and a τ_S minimum occurs at a normalized distance (x/h, where h is element height) of (downwind of the roughness leading edge is negative), then recovers to a relatively stable value. The location of the minimum appears to scale with element height and not roughness density. The force on the elements decreases exponentially with normalized downwind distance and this rate of change scales with the roughness density, with the rate of change increasing as roughness density increases. Average τ_S : τ values for the six roughness surfaces scale predictably as a function of roughness density and in accordance with a shear stress partitioning model. The shear stress partitioning model performed very well in predicting the amount of surface shear stress, given knowledge of the stated input parameters for these patches of roughness. As the shear stress partitioning relationship within the roughness appears to come into equilibrium faster for smaller roughness element sizes it would also appear the shear stress partitioning model can be applied with confidence for smaller patches of smaller roughness elements than those used in this experiment.     

陕西气象信息数据库应用平台综合设计

硬盘一旦出现故障，对每一个使用电脑的人来说简直就是一场灾难，因为硬盘里的数据比硬盘本身的价值更大，如何将其损失降到最低限度，这是非常值得研究的。     

中尺度模式和新一代天气雷达产品识别云降水方法

介绍一种结合中尺度模式产品和新一代天气雷达产品的云降水识别方法。首先利用中尺度模式(MM5)产品计算得到降水潜力区识别因子,分析降水潜力区。进一步结合新一代天气雷达产品,分析潜力区内云团的降水参数,确定有利于降水的云团。分析结果表明,潜力综合指标对未来可能出现的具有大量水资源的对流性系统具有指示意义,可以用于分析对流性系统的降水潜力;另一方面雷达降水等级是对实际对流云团水资源等级的划分。综合这两者进行降水潜力的分析判别,可以增强降水云的识别能力。     

T213数值预报产品在2003年宁夏首场透雨天气过程中的应用分析

2003年5月5～6日,宁夏出现了入春以来第一场透雨天气过程,本文利用常规天气资料和T213数值预报产品,对此次降水天气过程从主要影响系统和T213数值预报有关物理量预报场等方面进行分析发现,T213对我区降水有很好的预报能力和指示意义,并随着预报时效的缩短,其可信度在不断提高。     

Errors and correction of precipitation measurements in China

In order to discover the range of various errors in Chinese precipitation measurements and seek a correction method, 30 precipitation evaluation stations were set up countrywide before 1993. All the stations are reference stations in China. To seek a correction method for wind-induced error, a precipitation correction instrument called the ＂horizontal precipitation gauge＂ was devised beforehand. Field intercomparison observations regarding 29,000 precipitation events have been conducted using one pit gauge, two elevated operational gauges and one horizontal gauge at the above 30 stations. The range of precipitation measurement errors in China is obtained by analysis of intercomparison measurement results. The distribution of random errors and systematic errors in precipitation measurements are studied in this paper. A correction method, especially for wind-induced errors, is developed. The results prove that a correlation of power function exists between the precipitation amount caught by the horizontal gauge and the absolute difference of observations implemented by the operational gauge and pit gauge. The correlation coefficient is 0.99. For operational observations, precipitation correction can be carried out only by parallel observation with a horizontal precipitation gauge. The precipitation accuracy after correction approaches that of the pit gauge. The correction method developed is simple and feasible.     

A timescale decomposed threshold regression downscaling approach to forecasting South China early summer rainfall

A timescale decomposed threshold regression(TSDTR) downscaling approach to forecasting South China early summer rainfall(SCESR) is described by using long-term observed station rainfall data and NOAA ERSST data. It makes use of two distinct regression downscaling models corresponding to the interannual and interdecadal rainfall variability of SCESR.The two models are developed based on the partial least squares(PLS) regression technique, linking SCESR to SST modes in preceding months on both interannual and interdecadal timescales. Specifically, using the datasets in the calibration period 1915–84, the variability of SCESR and SST are decomposed into interannual and interdecadal components. On the interannual timescale, a threshold PLS regression model is fitted to interannual components of SCESR and March SST patterns by taking account of the modulation of negative and positive phases of the Pacific Decadal Oscillation(PDO). On the interdecadal timescale, a standard PLS regression model is fitted to the relationship between SCESR and preceding November SST patterns. The total rainfall prediction is obtained by the sum of the outputs from both the interannual and interdecadal models. Results show that the TSDTR downscaling approach achieves reasonable skill in predicting the observed rainfall in the validation period 1985–2006, compared to other simpler approaches. This study suggests that the TSDTR approach,considering different interannual SCESR-SST relationships under the modulation of PDO phases, as well as the interdecadal variability of SCESR associated with SST patterns, may provide a new perspective to improve climate predictions.     

Factors controlling the interannual variations of MJO intensity

The interannual variations of intensity of the Madden-Julian Oscillation (MJO) during boreal winter are investigated by using the observed outgoing longwave radiation (OLR) and the reanalysis data of ECMWF and NCEP. The standard deviation of 20-80-day filtered OLR anomaly is used to measure the MJO intensity. The dominant spatial structure of the interannual variability is revealed by an EOF analysis of the MJO intensity field. It is found that the leading mode is associated with eastern Pacific type ENSO, whereas the second mode is related to central Pacific type ENSO. A simple atmospheric model is used to investigate the relative roles of background moisture and wind changes in affecting the overall strength of MJO. The numerical experiments indicate that the background moisture effect is dominant while the background wind change has a minor effect.