The Weather Research and Forecasting Model's Community Variational/Ensemble Data Assimilation System: WRFDA

Barker, Dale; Huang, Xiang‐Yu; Liu, Zhiquan; Auligné, Thomas; Zhang, Xin; Rugg, Steven; Ajjaji, Raji; Bourgeois, Al; Bray, John; Chen, Yongsheng; Demirtaş, Meral; Guo, Yong-run; Henderson, Tom; Huang, Wei; Lin, Hui-Chuan; Michalakes, John; Rizvi, S. R. H.; Zhang, Xiaoyan

doi:10.1175/bams-d-11-00167.1

Cited by 410 publications

(309 citation statements)

References 49 publications

(25 reference statements)

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“…Third, this study designs a noncycling process, in which the first guesses are generated based on NCEP FNL data, while the first guesses in the cycling mode are typically obtained from short-range (typically 1-6 h) forecasts (Skamarock et al 2008, p.88). Fourth, the control variables (CVs) in the WRFDA system are streamfunction, unbalanced potential velocity, unbalanced temperature, unbalanced surface pressure, and pseudorelative humidity (Barker et al 2004). For all cases, the background error covariance was obtained by computing the average difference between 12-and 24-h forecasts valid at the same time using the National Meteorological Center (NMC) method (Parrish and Derber 1992).…”

Section: Model Configurations and Experiments Designmentioning

confidence: 99%

“…Because of the growing interest in the WRFDA system and associated community-based developments, the WRFDA system has been equipped with extensive capability to assimilate various types of observations. The WRFDA system has DA options such as three-dimensional variational data assimilation (3D-Var), 4D-Var, and hybrid variational-ensemble DA that permit assimilating a wide range of observations including in situ measurements, Doppler radar reflectivity, precipitation, and radiances (Barker et al 2012;Wang et al 2013). For example, the 3D-Var assimilation of conventional ground-based data and radiance observations has been used for improving precipitation forecasts at various spatial resolutions (Ha et al 2011;Ha and Lee 2012;Hsiao et al 2012;Liu et al 2012;Routray et al 2010;Schwartz et al 2012;Xu and Powell 2012).…”

Section: Introductionmentioning

confidence: 99%

“…This study uses version 3.4 of the WRF Model (see Skamarock et al 2008) and the WRFDA system (Barker et al 2004(Barker et al , 2012Huang et al 2009). Note that the WRFDA system is currently not fully capable of assimilating precipitation-affected radiances (Barker et al 2012), and thus, we only focus on the assimilation of precipitation for our dynamical downscaling experiments using the 4D-Var module.…”

Section: Introductionmentioning

confidence: 99%

“…Note that the WRFDA system is currently not fully capable of assimilating precipitation-affected radiances (Barker et al 2012), and thus, we only focus on the assimilation of precipitation for our dynamical downscaling experiments using the 4D-Var module. Specifically, we first focus on assimilating a point-scale observation at a single site to shed light on the sensitivity of dynamical downscaling to precipitation assimilation.…”

Section: Introductionmentioning

confidence: 99%

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Dynamical Precipitation Downscaling for Hydrologic Applications Using WRF 4D-Var Data Assimilation: Implications for GPM Era

Lin

Ebtehaj

Bras

et al. 2015

Journal of Hydrometeorology

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The objective of this study is to develop a framework for dynamically downscaling spaceborne precipitation products using the Weather Research and Forecasting (WRF) Model with four-dimensional variational data assimilation (4D-Var). Numerical experiments have been conducted to 1) understand the sensitivity of precipitation downscaling through point-scale precipitation data assimilation and 2) investigate the impact of seasonality and associated changes in precipitation-generating mechanisms on the quality of spatiotemporal downscaling of precipitation. The point-scale experiment suggests that assimilating precipitation can significantly affect the precipitation analysis, forecast, and downscaling. Because of occasional overestimation or underestimation of small-scale summertime precipitation extremes, the numerical experiments presented here demonstrate that the wintertime assimilation produces downscaled precipitation estimates that are in closer agreement with the reference National Centers for Environmental Prediction stage IV dataset than similar summertime experiments. This study concludes that the WRF 4D-Var system is able to effectively downscale a 6-h precipitation product with a spatial resolution of 20 km to hourly precipitation with a spatial resolution of less than 10 km in grid spacingrelevant to finescale hydrologic applications for the era of the Global Precipitation Measurement mission.

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Section: Model Configurations and Experiments Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamical Precipitation Downscaling for Hydrologic Applications Using WRF 4D-Var Data Assimilation: Implications for GPM Era

Lin

Ebtehaj

Bras

et al. 2015

Journal of Hydrometeorology

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“…The WRF data assimilation (WRFDA) system used in this study is a variational data assimilation system formulated in grid-point space (Barker et al 2012). In this system, U h is a recursive filter transform to impose the horizontal correlations, U v is the application of vertical correlations through empirical orthogonal functions (EOF) of analysis control variables, and U p changes the analysis control variables to model state variables using the statistical balance relationship.…”

Section: Variational Data Assimilationmentioning

confidence: 99%

Balance characteristics of multivariate background error covariances and their impact on analyses and forecasts in tropical and Arctic regions

Chen

Rizvi

Huang

et al. 2013

Meteorol Atmos Phys

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For variational data assimilation, the background error covariance matrix plays a crucial role because it is strongly linked with the local meteorological features, and is especially dominated by error correlations between different analysis variables. Multivariate background error (MBE) statistics have been generated for two regions, namely the Tropics (covering Indonesia and its neighborhood) and the Arctic (covering high latitudes). Detailed investigation has been carried out for these MBE statistics to understand the physical processes leading to the balance (defined by the forecasts error correlations) characteristics between mass and wind fields for the low and high latitudes represented by these two regions. It is found that in tropical regions, the unbalanced (full balanced) part of the velocity potential (divergent part of wind) contributes more to the balanced part of the temperature, relative humidity, and surface pressure fields as compared with the stream function (rotational part of wind). However, the exact opposite happens in the Arctic. For both regions, the unbalanced part of the temperature field is the main contributor to the balanced part of the relative humidity field. Results of single observation tests and six-hourly data assimilation cycling experiments are consistent with the respective balance part contributions of different fields in the two regions. This study provides an understanding of the contrasting dynamical balance relationship that exists between the mass and wind fields in high-and low-latitude regions. The study also examines the impact of MBE on Weather Research and Forecasting model forecasts for the two regions.

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A review of the remote sensing of lower tropospheric thermodynamic profiles and its indispensable role for the understanding and the simulation of water and energy cycles

Wulfmeyer

Hardesty

Turner

et al. 2015

Reviews of Geophysics

199

150

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A review of remote sensing technology for lower tropospheric thermodynamic (TD) profiling is presented with focus on high accuracy and high temporal-vertical resolution. The contributions of these instruments to the understanding of the Earth system are assessed with respect to radiative transfer, land surface-atmosphere feedback, convection initiation, and data assimilation. We demonstrate that for progress in weather and climate research, TD profilers are essential. These observational systems must resolve gradients of humidity and temperature in the stable or unstable atmospheric surface layer close to the ground, in the mixed layer, in the interfacial layer-usually characterized by an inversion-and the lower troposphere. A thorough analysis of the current observing systems is performed revealing significant gaps that must be addressed to fulfill existing needs. We analyze whether current and future passive and active remote sensing systems can close these gaps. A methodological analysis and demonstration of measurement capabilities with respect to bias and precision is executed both for passive and active remote sensing including passive infrared and microwave spectroscopy, the global navigation satellite system, as well as water vapor and temperature Raman lidar and water vapor differential absorption lidar. Whereas passive remote sensing systems are already mature with respect to operational applications, active remote sensing systems require further engineering to become operational in networks. However, active remote sensing systems provide a smaller bias as well as higher temporal and vertical resolutions. For a suitable mesoscale network design, TD profiler system developments should be intensified and dedicated observing system simulation experiments should be performed.

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The Weather Research and Forecasting Model's Community Variational/Ensemble Data Assimilation System: WRFDA

Cited by 410 publications

References 49 publications

Dynamical Precipitation Downscaling for Hydrologic Applications Using WRF 4D-Var Data Assimilation: Implications for GPM Era

Dynamical Precipitation Downscaling for Hydrologic Applications Using WRF 4D-Var Data Assimilation: Implications for GPM Era

Balance characteristics of multivariate background error covariances and their impact on analyses and forecasts in tropical and Arctic regions

A review of the remote sensing of lower tropospheric thermodynamic profiles and its indispensable role for the understanding and the simulation of water and energy cycles

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