利用CONUS第Ⅳ级规范雷达资料对26个降水数据集的逐日评价外文翻译资料

 2022-11-15 16:43:00

Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS

Hylke E. Beck1, Ming Pan1, Tirthankar Roy1, Graham P. Weedon2, Florian Pappenberger3, Albert I. J. M. van Dijk4, George J. Huffman5, Robert F. Adler6, and Eric F. Wood1

1Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, USA

2Met Office, JCHMR, Maclean Building, Benson Lane, Crowmarsh Gifford, Oxfordshire, UK

3European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK

4Fenner School for Environment and Society, Australian National University, Canberra, Australia

5NASA Goddard Space Flight Center (GSFC), Greenbelt, Maryland, USA

6University of Maryland, Earth System Science Interdisciplinary Center, College Park, Maryland, USA

Correspondence: Hylke E. Beck (hylke.beck@gmail.com)

Abstract.

New precipitation (P ) datasets are released regularly, following innovations in weather forecasting models, satellite retrieval methods, and multi-source merging techniques. Using the conterminous US as a case study, we evaluated the performance of 26 gridded (sub-)daily P datasets to obtain insight in the merit of these innovations. The evaluation was performed at a daily

5 timescale for the period 2008–2017 using the Kling-Gupta Efficiency (KGE), a performance metric combining correlation, bias, and variability. As reference, we used the high-resolution (4 km) Stage-IV gauge-radar P dataset. Among the three KGE com- ponents, the P datasets performed worst overall in terms of correlation (related to event identification). In terms of improving KGE scores for these datasets, improved P totals (affecting the bias score) and improved distribution of P intensity (affecting the variability score) are of secondary importance. Among the 11 gauge-corrected P datasets, the best overall performance was

10 obtained by MSWEP V2.2, underscoring the importance of applying daily gauge corrections and accounting for gauge report- ing times. Several uncorrected P datasets outperformed gauge-corrected ones. Among the 15 uncorrected P datasets, the best performance was obtained by the fourth-generation reanalysis ERA5-HRES, reflecting the significant advances in earth system modeling during the last decade. IMERGHH V05 performed substantially better than TMPA-3B42RT V7, attributable to the many improvements implemented in the IMERG satellite P retrieval algorithm. IMERGHH V05 outperformed ERA5-HRES

15 in regions dominated by convective storms, while the opposite was observed in regions of complex terrain. The ERA5-EDA ensemble average exhibited higher correlations than the ERA5-HRES deterministic run, highlighting the value of ensem- ble modeling. The regional convection-permitting climate model WRF showed considerably more accurate P totals over the mountainous west and performed best among the uncorrected datasets in terms of variability, suggesting there is merit in using high-resolution models to obtain climatological P statistics. Our findings can be used as a guide to choose the most suitable P

20 dataset for a particular application.

1 Introduction

Knowledge about the spatio-temporal distribution of precipitation (P ) is important for a multitude of scientific and operational applications, including flood forecasting, agricultural monitoring, and disease tracking (Tapiador et al., 2012; Kucera et al., 2013; Kirschbaum et al., 2017). However, P is highly variable in space and time and therefore extremely challenging to

5 estimate, especially in topographically complex and convection-dominated regions (Stephens et al., 2010; Herold et al., 2016; Prein and Gobiet, 2017). In the past decades, numerous gridded P datasets have been developed, differing in terms of design objective, spatio-temporal resolution and coverage, data sources, algorithm, and latency (see Tables 1 and 2 for an overview of quasi- and fully-global datasets).

A large number of regional-scale studies have evaluated gridded P datasets to obtain insight in the merit of different methods

10 and innovations (see reviews by Gebremichael, 2010, Maggioni et al., 2016, and Sun et al., 2018). However, many of these studies: (i) used only a subset of the available P datasets, and omitted (re)analyses, which have higher skill in cold periods and regions (Huffman et al., 1995; Ebert et al., 2007; Beck et al., 2017c); (ii) focused on a small (sub-continental) region, limiting the generalizability of the findings; (iii) considered a small number (lt; 50) of rain gauges or streamflow gauging stations for the evaluation, limiting the validity of the findings; (iv) used gauge observations already incorporated in the datasets as reference

15 without explicitly mentioning this, potentially leading to a biased evaluation; and (v) failed to account for gauge reporting times, possibly resulting in spurious temporal mismatches between the datasets and the gauge observations.

In an effort to obtain more generally valid conclusions, we recently evaluated 22 (sub-)daily gridded P datasets using

gauge observations (sim;75 000 stations) and hydrological modeling (sim;9000 catchments) globally (Beck et al., 2017c). Other noteworthy large-scale assessments include Massari et al. (2017), who evaluated five P datasets using triple collocation at

20 the daily time scale without the use of ground observations, and Sun et al. (2018), who compared 19 P datasets from daily to annual timescales. These comprehensive studies highlighted (among other things): (i) substantial differences among P datasets and thus the importance of dataset choice; (ii

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利用CONUS第Ⅳ级规范雷达资料对26个降水数据集的逐日评价

摘要

随着天气预报模型、卫星检索方法和多源合并技术的革新,出现了很多新的定期发布降水数据集(P)。我们评估了26个网格(子网格)每日P数据集的性能,以洞察这些创新的优点。使用Kling-Gupta方法(KGE)对2008-2017年期间的数据以日时间尺度进行评估,KGE是结合相关系数、平均误差和变率比值的方法。作为参考,我们使用了高分辨率(4km)的IV级测距雷达P数据集。在三个KGE组件中,P数据集在相关系数(与事件标识相关)方面总体表现最差。就改善这些数据集的KGE评分而言,改进的P总数(影响平均误差)和改进的P强度分布(影响变率比值)是次要的。在11个规范校正的P数据集中,总体性能最好的数据是由MSWEP V2.2获得的,强调了应用每日量规校正和计算量规报告时间的重要性。几个未经校正的P数据集的表现优于规范校正的P数据集。在15个未校正的P数据中,第四代再分析ERA5-HRES获得了最佳性能的数据,这反映了过去十年地球系统建模的重大进展。IMERGHH V05的性能明显优于TMPA-3B42RT V7,这归因于在IMERG卫星P检索算法中实现的许多改进。在以对流风暴为主的地区,IMERGHH V05优于ERA5-HRES。地形复杂的地区则相反。ERA5-EDA系综平均表现出比ERA5-HRES确定性运行更高的相关性,突出了系综建模的价值。区域对流允许气候模式WRF在西部山区显示出相当精确的P总量,并且在未校正的数据集中,在变率比值方面表现最好,表明使用高分辨率模式来获得气候P统计量是有价值的。本研究结果可作为选择最适P用于特定应用程序的数据集。

1.引言

关于降水时空分布的数据(P)对于包括洪水预报、农业监测和疾病跟踪在内的许多科学和操作应用是非常重要的(Tapiador等人,2012;Kucera等人,2013;Kirschbaum等人,2017)。然而,降水时空分布的数据在空间和时间上高度可变,因此对降水时空分布数据的估计非常具有挑战性,特别是在地形复杂和对流占主导的地区(Stephens等人,2010;Herold等人,2016;Prein和Gobiet,2017)。在过去几十年中,已经开发了许多网格化P数据集,在设计目标、时空分辨率和覆盖范围、数据源、算法和延迟方面存在差异(关于准和全局数据集的概述,参见表1和表2)。许多区域规模的研究已经评估了网格化P数据集,以了解不同方法的优点以及创新(参见Gebremichael,2010,Maggioni等人,2016年和Sun等人的评论,2018年)。然而,这些研究中有许多研究有以下问题:

(1)只使用了可用降水时空分布的数据P数据集的一个子集,而省略了分析,这些分析在寒冷时期和寒冷地区具有更高的可操作性;

(2)集中于一个小的(次大陆)区域,限制了调查结果的概括性;

(3)考虑到少量(lt; 50)雨量器或流量器站进行评价,限制了调查结果的有效性;

(4)使用已纳入数据集的测量观测值作为参考,但未明确提及这一点,可能导致评价有偏见;

(5)未能考虑到测量报告时间,可能导致数据集与测量观察值之间的虚假时间不匹配。

为了获得更加普遍有效的结论,我们最近使用以下方法评估了22(子级)每日网格化P数据集全球测量观测(75,000个站)和水文模拟(9000个流域)(Beck等人,2017c)。为了获得更广泛有效的结论,我们评估最近22每日网格P使用计观测数据集(75sim;000站)和水文建模(sim;9000集雨)全球的数据。其他值得注意的大规模评估包括Massari等人(2017),他们在不使用地面观测数据的情况下,以20天为一个时间尺度,使用三组数据对5个P数据集进行了评估;Sun等人(2018)比较了19个P数据集,从每天到每年的时间尺度。这些综合研究强调:

(i)P数据集之间的显著差异,突出了数据集选择的重要性;

(ii)卫星和分析P数据集的互补优势;

(iii)合并来自不同来源的P估计数的价值;

(iv)每日(相对于每月)量规修正的效力;

(v)山区P值普遍低估。

在这里,我们评估了一个更大的(亚)日(准)全球P数据集的选择,包括一些有希望的最近发布的数据集:ERA5 (ERA5, ERA-Interim的后续版本;Hersbach and Dee, 2016), IMERG (TMPA的继承者;和MERRA-2,为数不多的重新分析P数据集的数据集之一,这些数据集包含了日常的测量数据;Gelaro等,2017;Reichle等人,2017年)。此外,我们评估了区域对流允许气候模型(WRF)的性能。作为参考,我们使用了由国家环境预测中心(NCEP)制作的高分辨率、基于雷达的、经测量调整的30 Stage-IV P数据集(Lin和Mitchell, 2005)。作为性能指标,我们采用了广泛使用的Kling-Gupta方法。我们通过讨论9个相关问题来揭示不同P数据集的优缺点以及不同技术和方法创新的优点:

1.决定高KGE分数最重要的因素是什么?

2.未更正的P数据集如何执行?

3.基于gauge的P数据集执行情况如何?

4.所示量规修正的影响是什么?

5.IMERG对TMPA的改善是什么?

6.ERA5相对于ERA-Interim的改进是什么?

7.ERA5-EDA集成平均与ERA5-HRES确定性运行如何比较?

8.IMERG和ERA5比较如何?

9.一个允许对流的区域气候模型表现如何?

2数据与方法

2.1 P数据集

我们评估了26个网格日P数据集(表1和表2)的性能。除了WRF,它仅限于美国大陆。这些数据集被归类为未更正的数据集,这意味着时间变化完全取决于卫星和/或(重新)分析数据,或经过修正,这意味着时间变化这在一定程度上取决于观测结果。我们包含了7个完全基于卫星数据的数据集(CMORPH V1.0,GSMaP-Std V6, IMERGHHE V05, PERSIANN, PERSIANN- ccs, sm2raincci V2,和TMPA-3B42RT V7),全部6个基于(re)分析(ERA-Interim, ERA5- hres, ERA5- eda, GDAS-Anl, JRA-55和NCEP-CFSR),尽管ERA5具有同化作用,包括卫星和(re)分析数据(CHIRP V2.0),一种基于区域对流允许气候模型(WRF)。

在基于测量的P数据集中,有6个测量和卫星数据,1个量规和再分析数据(WFDEI-GPCC),三个联合测量,卫星和分析数据(CHIRPS V2.0, MERRA-2和MSWEP V2.2)。其中一个完全基于测量观察(CPC统一V1.0/RT)。为了透明度和再现性,我们报告数据集在整个研究过程中,为这些信息提供的数据集提供版本号。为P数据集与子日时间分辨率,我们计算了世界时00:00-23:59的日累积。P数据集空间分辨率lt; 0.1度,重新采样到0.1◦使用双线性平均,而那些空间决议gt; 0.1◦重新取样。进行双线性插值。

2.2 Stage-IV gaug -radar数据

作为参考,我们使用了NCEP Stage-IV数据集,该数据集具有4公里的空间和小时时间分辨率,涵盖了2002年到现在,从140雷达和sim;5500和合并数据指标。NCEP Stage-IV数据集提供的P的估计值非常精确,因此被广泛用于P数据集的评估。日阶段NCEP Stage-IV数据集的数据是可用的,但他们表示与我们正在评估的数据集积累周期不兼容(12:00-11:59 UTC,而不是00:00 -23:59 UTC)。因此,我们计算了00:00-23:59 UTC的每日累积时间,从6小时级NCEP Stage-IV数据集使用双线性平均累计计算的Stage-IV数据集从原来的4公里极坐标立体投影重新投影到常规地理0.1◦网格。

为了减少系统偏差,对Stage-IV数据集进行了重新调整,使其长期平均值与PRISM值相匹配评估期(2008 - 2017)的数据集。为此,棱镜数据集从sim;800高档0.1◦使用双线性平均。棱镜数据集是由测量观测插值得到的,一般认为美国最精确的每月P数据集。然而,该数据集还没有校正风致仪表,在一定程度上低估了P的数值。

2.3评价方法

通过计算评估执行每天的时间和0.1◦空间分辨率,为每个网格单元从2008年到2017年的十年间,每天时间序列的效率(KGE)得分。KGE是一个客观的表现,结合相关性、偏差和可变性的度量。

定义如下:

r是由相关的组件(Pearson)的相关系数、偏差beta;和可变性gamma;的比率估计和观察到的系数

的变化:

评估前,每日累计计算了具有亚日时间分辨率的P数据集。另外,P数据集具有空间分辨率gt; 0.1◦被重新取样使用,而P与空间分辨率数据集lt; 0.1◦是重新取样。0.1◦使用双线性插值。

3结果与讨论

3.1决定高KGE分数最重要的因素是什么?

表1.本研究中评价的15个未校正(准)全球(亚)日网格P数据集概述。表2列出了11个规范修正的数据集。数据源中的缩写( (S)列定义为:S,卫星;R,再分析;A,分析;M,区域气候模式。时态覆盖栏中的首字母缩写NRT表示“接近实时”。在空间覆盖栏中 “全球”是指包括海洋在内的全面全球覆盖,而“陆地”则意味着覆盖范围仅限于陆地表面。

Name Details Data Spatial Spatial Temporal Temporal Reference or website source(s) resolution coverage resolution coverage

CHIRP V2.01 Climate Hazards group InfraRed Precipitation (CHIRP) V2.0

S, R, A 0.05 Land, 50N/S Daily 1981–NRT3 Funk et al. (2015a)

CMORPH V1.0 CPC MORPHing technique (CMORPH) V1.0 S 0.07 60N/S 30 minutes 1998–NRT2 Joyce et al. (2004); Xie

et al. (2017)

ERA-Interim European Centre for Medium-range Weather Forecasts ReAnalysis Interim (ERA-Interim)

ERA5-HRES5 European Centre for Medium-range Weather Forecasts ReAnalysis 5 (ERA5) High RESolution (HRES)

ERA5-EDA5 European Centre for Medium-range Weather Forecasts ReAnalysis 5 (ERA5) Ensemble Data Assimilation (EDA) ensemble mean

GDAS-Anl National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS) Analysis (Anl)

GSMaP-Std V6 Global Satellite Mapping of Precipitation (GSMaP) Mov- ing Vector with Kalman (MVK) Standard V6

IMERGHHE V05 Integrated Multi-satellitE Retrievals for GPM (IMERG) early run V05

R sim;0.75

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