Moisture Recycling over the Iberian Peninsula: The Impact of 3DVAR Data Assimilation

被引:2
|
作者
Gonzalez-Roji, Santos J. [1 ,2 ]
Saenz, Jon [3 ,4 ]
Diaz de Argandona, Javier [5 ]
Ibarra-Berastegi, Gabriel [4 ,6 ]
机构
[1] Univ Bern, Oeschger Ctr Climate Change Res, CH-3012 Bern, Switzerland
[2] Univ Bern, Climate & Environm Phys, CH-3012 Bern, Switzerland
[3] Univ Basque Country UPV EHU, Dept Appl Phys 2, Leioa 48940, Spain
[4] Univ Basque Country PiE UPV EHU, Plentzia Itsas Estazioa, Plentzia 48620, Spain
[5] Univ Basque Country UPV EHU, Dept Appl Phys 1, Vitoria 01006, Spain
[6] Univ Basque Country UPV EHU, Bilbao Engn Sch, Dept NE & Fluid Mech, Bilbao 48013, Spain
关键词
moisture recycling; recycling; data assimilation; WRF; iberian peninsula; ATMOSPHERIC WATER-VAPOR; CENTRAL UNITED-STATES; INTERANNUAL VARIABILITY; PART II; CUMULUS PARAMETERIZATION; WINTER PRECIPITATION; LAGRANGIAN ANALYSIS; TEMPORAL PATTERNS; CLIMATE; EUROPE;
D O I
10.3390/atmos11010019
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
In this paper, we have estimated the spatiotemporal distribution of moisture recycling over the Iberian Peninsula (IP). The recycling ratio was computed from two simulations over the IP using the Weather Research and Forecasting (WRF) model with a horizontal resolution of 15 km spanning the period 2010-2014. The first simulation (WRF N) was nested inside the ERA-Interim with information passed to the domain through the boundaries. The second run (WRF D) is similar to WRF N, but it also includes 3DVAR data assimilation every six hours (12:00 a.m., 6:00 a.m., 12:00 p.m. and 6:00 p.m. UTC). It was also extended until 2018. The lowest values of moisture recycling (3%) are obtained from November to February, while the highest ones (16%) are observed in spring in both simulations. Moisture recycling is confined to the southeastern corner during winter. During spring and summer, a gradient towards the northeastern corner of the IP is observed in both simulations. The differences between both simulations are associated with the dryness of the soil in the model and are higher during summer and autumn. WRF D presents a lower bias and produces more reliable results because of a better representation of the atmospheric moisture.
引用
收藏
页数:19
相关论文
共 50 条
  • [31] Intercomparison of the impact of INSAT-3D atmospheric motion vectors in 3DVAR and hybrid ensemble-3DVAR data assimilation systems during Indian summer monsoon
    Gogoi, Rekha Bharali
    Kutty, Govindan
    Boroghain, Arup
    THEORETICAL AND APPLIED CLIMATOLOGY, 2021, 145 (1-2) : 585 - 596
  • [32] Impact of a Diagnostic Pressure Equation Constraint on Tornadic Supercell Thunderstorm Forecasts Initialized Using 3DVAR Radar Data Assimilation
    Ge, Guoqing
    Gao, Jidong
    Xue, Ming
    ADVANCES IN METEOROLOGY, 2013, 2013
  • [33] Intercomparison of the impact of INSAT-3D atmospheric motion vectors in 3DVAR and hybrid ensemble-3DVAR data assimilation systems during Indian summer monsoon
    Rekha Bharali Gogoi
    Govindan Kutty
    Arup Boroghain
    Theoretical and Applied Climatology, 2021, 145 : 585 - 596
  • [34] A comparison of limited-area 3DVAR and ETKF-En3DVAR data assimilation using radar observations at convective scale for the prediction of Typhoon Saomai (2006)
    Shen, Feifei
    Xue, Ming
    Min, Jinzhong
    METEOROLOGICAL APPLICATIONS, 2017, 24 (04) : 628 - 641
  • [35] Moisture recycling in the Iberian Peninsula from a regional climate simulation: Spatiotemporal analysis and impact on the precipitation regime
    Rios-Entenza, Alexandre
    Soares, Pedro M. M.
    Trigo, Ricardo M.
    Cardoso, Rita M.
    Miguez-Macho, Gonzalo
    JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2014, 119 (10) : 5895 - 5912
  • [36] Predictive skill comparative assessment of WRF 4DVar and 3DVar data assimilation: An Indian Ocean tropical cyclone case study
    Tiwari, Gaurav
    Kumar, Pankaj
    ATMOSPHERIC RESEARCH, 2022, 277
  • [37] Comparisons of Hybrid En3DVar with 3DVar and EnKF for Radar Data Assimilation: Tests with the 10 May 2010 Oklahoma Tornado Outbreak
    Kong, Rong
    Xue, Ming
    Liu, Chengsi
    Jung, Youngsun
    MONTHLY WEATHER REVIEW, 2021, 149 (01) : 21 - 40
  • [38] Assimilation of HY-2A scatterometer sea surface wind data in a 3DVAR data assimilation system—A case study of Typhoon Bolaven
    Yi Yu
    Weimin Zhang
    Zhongyuan Wu
    Xiaofeng Yang
    Xiaoqun Cao
    Mengbin Zhu
    Frontiers of Earth Science, 2015, 9 : 192 - 201
  • [39] Optimization and Evaluation of SO2 Emissions Based on WRF-Chem and 3DVAR Data Assimilation
    Hu, Yiwen
    Zang, Zengliang
    Chen, Dan
    Ma, Xiaoyan
    Liang, Yanfei
    You, Wei
    Pan, Xiaobin
    Wang, Liqiong
    Wang, Daichun
    Zhang, Zhendong
    REMOTE SENSING, 2022, 14 (01)
  • [40] Direct Assimilation of Radar Reflectivity Data Using 3DVAR: Treatment of Hydrometeor Background Errors and OSSE Tests
    Liu, Chengsi
    Xue, Ming
    Kong, Rong
    MONTHLY WEATHER REVIEW, 2019, 147 (01) : 17 - 29