Global Meteorological Forcing Dataset for Land Surface Modeling

A global dataset of meteorological forcings has been developed that can be used to drive models of land surface hydrology. The dataset is constructed by combining a suite of global observation-based datasets with the NCEP/NCAR reanalysis. Known biases in the reanalysis precipitation and near-surface meteorology have been shown to exert an erroneous effect on modeled land surface water and energy budgets and are thus corrected using observation-based datasets of precipitation, air temperature and radiation. Corrections are also made to the rain day statistics of the reanalysis precipitation which have been found to exhibit a spurious wave-like pattern in high-latitude wintertime. Wind-induced low measurement of solid precipitation is removed using the results from the World Meteorological Organization (WMO) Solid Precipitation Measurement Intercomparison. Precipitation is disaggregated in space to 1.0 degree and 0.25 degree by statistical downscaling using relationships developed with the Global Precipitation Climatology Project (GPCP) daily product. Disaggregation in time from daily to 3-hourly is accomplished similarly, using the Tropical Rainfall Measuring Mission (TRMM) 3-hourly real-time dataset. Other meteorological variables (downward short- and longwave, specific humidity, surface air pressure and wind speed) are downscaled in space with account for changes in elevation. The dataset is evaluated against the bias-corrected forcing dataset of the second Global Soil Wetness Project. The final product provides a long-term, globally-consistent dataset of near-surface meteorological variables that can be used to drive models of the terrestrial hydrologic and ecological processes for the study of seasonal and interannual variability and for the evaluation of coupled models and other land surface prediction schemes.

已开发出一套全球气象强迫数据集，可用于驱动陆面水文模型。该数据集通过将一系列基于全球观测的数据集与NCEP/NCAR再分析资料（NCEP/NCAR Reanalysis）融合构建而成。研究表明，再分析资料中的降水与近地面气象要素存在已知偏差，此类偏差会对陆面水文与能量收支的模拟结果产生误导性影响，因此本数据集依托降水、气温及辐射的观测数据集对这些偏差开展校正工作。此外，研究还发现再分析降水的降雨日统计特征在高纬度冬季存在虚假的波浪状分布模式，因此也针对该特征进行了校正。利用世界气象组织（World Meteorological Organization, WMO）固态降水测量比对项目的研究成果，校正了因风力扰动导致的固态降水测量值偏低问题。借助基于全球降水气候计划（Global Precipitation Climatology Project, GPCP）逐日产品构建的统计降尺度关系，将降水数据在空间维度分别降尺度至1.0°和0.25°的分辨率。同理，依托热带降雨测量任务（Tropical Rainfall Measuring Mission, TRMM）的逐3小时实时数据集，将降水数据在时间维度从逐日分辨率降尺度至逐3小时分辨率。其余气象要素（向下短波辐射、向下长波辐射、比湿、地面气压与风速）则结合海拔高程变化完成空间降尺度处理。本数据集参照第二次全球土壤湿度项目（Global Soil Wetness Project, GSWP）的偏差校正强迫数据集进行了验证评估。最终产出的数据集提供了一套长期、全球一致的近地面气象要素序列，可用于驱动陆地水文与生态过程模型，以开展季节及年际变率相关研究，同时也可用于评估耦合模型及其他陆面预报方案。

搜集汇总

背景与挑战

背景概述

这是一个用于驱动陆地表面水文模型的全球气象强迫数据集，通过融合观测数据和再分析数据，校正降水、气温和辐射的偏差，提供1948年至2010年的长期全球一致数据。数据集包含多种时空分辨率（如0.25度、3小时），覆盖气温、湿度、辐射等关键气象变量，适用于研究季节和年际变异性及评估耦合模型。