Global Soil Wetness Project Phase 3 Atmospheric Boundary Conditions (Experiment 1)

retrospective atmospheric boundary conditions (9 variables: Rainfall, Snowfall, 2m Air Temperature, 2m Specific Humidity, Surface Pressure, Downward Shortwave Radiation, Downward Longwave Radiation, 10m Wind Speed, and Cloud Cover Fraction) for 1901-2010 in 3-hourly resolution are generated. 20th Century Reanalysis (20CR) [compo2011] [Compo el al., 2011] on global 2° resolution is dynamically downscaled into T248 (~0.5°) grid using a spectral nudging technique [Yoshimura and Kanamitsu 2008] in a Global Spectral Model (GSM) [Figure 2]. This successfully keeps the low frequency signal of original reanalysis, providing additional high frequency signals, which are lacking in previous products [e.g., Weedon et al., 2011]. It is essential to investigate phenomena at higher spatiotemporal scales such as extreme events. In order to relieve known artifacts (e.g., ripple patterns and persistent overcast in high latitude region), additional techniques, such as single ensemble correction [Yoshimura and Kanamitsu, 2013] and vertically weighted damping [Hong and Chang, 2012], are applied. Model biases in the downscaled 20CR are corrected using observational data (e.g., GPCC for precipitation, SRB for short/long wave radiation, and CRU for air temperature and daily temperature range). In addition to previously introduced bias correction algorithms [e.g., Weedon et al., 2011], variability in higher temporal (<month) resolution is carefully corrected [Kim et al., in preparation]. Also, wind-induced precipitation undercatch correction is applied considering different types of gauges and their global distribution [Hirabayashi et al., 2008]. Through the above mentioned data generation strategy, GSWP3 has further reliability and consistency over the century long target timespan with higher spatiotemporal resolutions.

本研究生成了1901-2010年逐3小时分辨率的回顾式大气边界条件数据集，共包含9个变量：降雨（Rainfall）、降雪（Snowfall）、2米气温（2m Air Temperature）、2米比湿（2m Specific Humidity）、地面气压（Surface Pressure）、向下短波辐射（Downward Shortwave Radiation）、向下长波辐射（Downward Longwave Radiation）、10米风速（10m Wind Speed）以及云量分数（Cloud Cover Fraction）。研究团队采用谱逼近技术（spectral nudging technique），依托全球谱模式（Global Spectral Model, GSM）[见图2]，将全球2°分辨率的20世纪再分析资料（20th Century Reanalysis, 20CR）[compo2011；Compo等，2011]动力降尺度至T248（约0.5°）网格。该方法成功保留了原始再分析资料的低频信号，同时补充了现有相关产品[如Weedon等，2011]所缺失的高频信号，这对于研究极端事件等更高时空尺度的现象至关重要。为缓解已知的伪影问题（如高纬度地区的波纹图案与持续性阴天），研究团队采用了单集合校正[Yoshimura与Kanamitsu，2013]、垂直加权阻尼[Hong与Chang，2012]等额外校正技术。研究团队利用观测资料对降尺度后的20CR进行模式偏差校正，其中降水资料采用全球降水气候学中心（GPCC）数据集、短波/长波辐射资料采用地球辐射预算（SRB）数据集、气温与气温日较差资料采用气候研究单位（CRU）数据集。除此前采用的偏差校正算法[如Weedon等，2011]外，研究团队还对低于月尺度的更高时间分辨率变率进行了精细化校正[Kim等，待发表]。同时，考虑到全球不同类型雨量计的分布差异，研究团队还实施了风力诱导的降水捕获不足校正[Hirabayashi等，2008]。通过上述数据生成方案，GSWP3在百年目标时间跨度内具备更高的时空分辨率，且可靠性与一致性进一步提升。

搜集汇总

背景与挑战

背景概述

该数据集提供了1901-2010年间的高时空分辨率大气边界条件数据，包含9个关键气象变量，适用于极端事件等高频现象的研究。数据通过动态降尺度和多源观测数据校正，确保了长期时间跨度的可靠性和一致性。