当前位置: X-MOL 学术J. Geophys. Res. Atmos. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Probabilistic Spatial Meteorological Estimates for Alaska and the Yukon
Journal of Geophysical Research: Atmospheres ( IF 4.4 ) Pub Date : 2020-10-24 , DOI: 10.1029/2020jd032696
Andrew J. Newman 1 , Martyn P. Clark 1, 2, 3 , Andrew W. Wood 1 , Jeffrey R. Arnold 4
Affiliation  

It is challenging to develop observationally based spatial estimates of meteorology in Alaska and the Yukon. Complex topography, frozen precipitation undercatch, and extremely sparse in situ observations all limit our capability to produce accurate spatial estimates of meteorological conditions. In this Arctic environment, it is necessary to develop probabilistic estimates of precipitation and temperature that explicitly incorporate spatiotemporally varying uncertainty and bias corrections. In this paper we exploit the recently developed ensemble Climatologically Aided Interpolation (eCAI) system to produce daily historical estimates of precipitation and temperature across Alaska and the Yukon Territory at a 2 km grid spacing for the time period 1980–2013. We extend the previous eCAI method to address precipitation gauge undercatch and wetting loss, which is of high importance for this high‐latitude region where much of the precipitation falls as snow. Leave‐one‐out cross‐validation shows our ensemble has little bias in daily precipitation and mean temperature at the station locations, with an overestimate in the daily standard deviation of precipitation. The ensemble is statistically reliable compared to climatology and can discriminate precipitation events across different precipitation thresholds. Long‐term mean loss adjusted precipitation is up to 36% greater than the unadjusted estimate in windy areas that receive a large fraction of frozen precipitation, primarily due to wind induced undercatch. Comparing the ensemble mean climatology of precipitation and temperature to PRISM and Daymet v3 shows large interproduct differences, particularly in precipitation across the complex terrain of southeast and northern Alaska.

中文翻译:

阿拉斯加和育空地区的概率空间气象估计

在阿拉斯加和育空地区发展基于观测的气象空间估计具有挑战性。复杂的地形,冻结的降水不足和原地观测极为稀疏,都限制了我们对气象条件进行准确的空间估算的能力。在这种北极环境中,有必要发展降水和温度的概率估计,其中应明确纳入时空变化的不确定性和偏差校正。在本文中,我们利用最新开发的集成气候辅助插值(eCAI)系统,以1980-2013年的时间为基础,以2 km的网格间隔生成阿拉斯加和育空地区的降水和温度的每日历史估计值。我们扩展了以前的eCAI方法,以解决降水量表欠补和湿润损失的问题,这对于这个高纬度地区非常重要,在该地区大部分降水都是降雪的。留一法交叉验证表明我们的集合对日降水量和测站位置的平均温度几乎没有偏差,而对降水的日标准偏差却有高估。与气候相比,该集合在统计上是可靠的,并且可以区分不同降水阈值之间的降水事件。长期平均损失调整后的降水比大部分未经冻结的大风地区的未调整估计高36%,这主要是由于风致不足引起的。
更新日期:2020-11-17
down
wechat
bug