Institute of Atmospheric Physics
本文利用区域气候模式RIEMS输出的3公里高分辨率格点数据和站点降水记录分析了中国西北黑河流域降水的动力降尺度和统计-动力降尺度问题，检验了多种不同因子组合下的多元线性回归（MLR）和贝叶斯模式平均（BMA）降尺度模型，在评估了其降尺度降水误差、与观测值相关系数、方差百分率和“负降水”偏差等方面的统计特征。结果表明，动力降尺度降水相关系数最高，但误差也最大，降水方差过大，达到观测值的1.5~2倍；而仅用700hPa 位势高度场、经向风和比湿等构建的统计降尺度模型估计降水的相关系数较低，误差较大，当在统计降尺度模型中引入动力降尺度降水因子后降水估计得到明显改善。MLR类型降水相关系数和方差百分率明显高于BMA模型，降水估计误差和“负降水”出现频次也明显大于后者。在黑河流域的“负降水”偏差主要出现在降水稀少冬半年，且中、下游出现频次较高，上游较低， 其中MLR类模型“负降水”频次较高，BMA模型频次较低，后者仅出现在黑河中、下游地区。包含动力降尺度降水因子的模型在一定程度上减少了“负降水”出现的频次。此外，降尺度模型估计降水的误差、方差百分率和相关系数等都随季节变化，其中动力降尺度降水误差最大，7种降尺度模型的降水相对误差在黑河中下游及冬半年最大，夏季最小。这说明极端干旱区或降水稀少季节的降水降尺度仍然需要进一步改进。这些评估表明，即使高分辨率的动力降尺度降水也存在明显偏差，需要用统计降尺度模型等对其订正，以便进一步降低站点降水估计的不确定性。
This paper focuses on the dynamic and statistic-dynamic downscaling techniques for estimating the precipitation at the stations in Heihe river basin of Northwest China depending on local observations at 14 sites and the outputs from a regional climate model (RIEMS2.0) with a resolution of 3 km×3km grids. The precipitation estimated further respectively by a multiple regression (MLR) and a Bayesian Model Average(BMA) with different factor combination is tested on the assessment indices as the errors, variance, "negative precipitation bias" and correlation coefficient with observation. Results show that the precipitation produced by the dynamic model is of the biggest errors, the most significant coherence, much large variance than observation by factor of 2 about, while big errors, low correlation coefficient and lower variance than observation are estimated by the statistically downscaling model with the factors as geopotential height, v-wind and specific humidity on 700hPa. When the model precipitation is introduced into the statistically downscaling models, the indices become improved, in which the correlation and variance percentage of MLR"s models are much more higher than BMA"s, so do the errors and "negative precipitation bias". The negative precipitation produced by the statistically downscaling models appears manly in cold season or in dry- and extremely dry land such as lower reaches of the river, of which the negative precipitation frequency become decreased if the model precipitation is added as a factor in the downscaling models. Besides, the statistical assessment of the monthly precipitation estimated from the downscaling models reveals that the four indices would be evolving with season, in which the errors of the dynamical downscaling is also the biggest among the downscaling models, and their relative errors are smaller in summer and bigger in cold season, especially in lower reaches of the river. It implies that the precipitation downscaling in dry land or dry season is still a difficult task to be studied further. These results show that a significant bias exists in the dynamic downscaling even for the regional climate model with high resolution. So, the statistic downscaling has to be combined with the regional model for decreasing the uncertainties of the precipitation estimated in the river basin.