Observational operators for dual polarimetric radars in variational data assimilation systems (PolRad VAR v1.0)

Kawabata, Takuya; Schwitalla, Thomas; Adachi, Ahoro; Bauer, Hans‐Stefan; Wulfmeyer, Volker; Nagumo, Nobuhiro

doi:10.5194/gmd-11-2493-2018

Cited by 16 publications

(16 citation statements)

References 38 publications

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“…The DSD is modeled by an exponential distribution. After performing the integration, the contribution from pure rainwater, Z r , is written in a simple form (Zhang et al, 2001;Posselt et al, 2015;Kawabata et al, 2018) as follows:…”

Section: Contribution From Rainwatermentioning

confidence: 99%

“…Despite the difficulty, some efforts have been undertaken for reflectivity assimilation with the TL/AD operators (Gao and Stensrud, 2012;Kawabata et al, 2018;Liu et al, 2019;Xiao et al, 2007), and reasonable results have been obtained in terms of hydrometeor analysis and precipitation forecasts. However, none of these studies employed operators as complex as those proposed by J08.…”

mentioning

confidence: 99%

“…However, none of these studies employed operators as complex as those proposed by J08. Kawabata et al (2018) adopted the expressions of Zhang et al (2001) and developed the TL/AD operator for C-band radar but without taking into account the contributions from ice-phase species.…”

mentioning

confidence: 99%

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A radar reflectivity operator with ice-phase hydrometeors for variational data assimilation (version 1.0) and its evaluation with real radar data

Wang

Liu

2019

Geosci. Model Dev.

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A reflectivity forward operator and its associated tangent linear and adjoint operators (together named Radar-Var) were developed for variational data assimilation (DA). RadarVar can analyze both rainwater and ice-phase species (snow and graupel) by directly assimilating radar reflectivity observations. The results of three-dimensional variational (3D-Var) DA experiments with a 3 km grid mesh setting of the Weather Research and Forecasting (WRF) model showed that RadarVar was effective at producing an analysis of reflectivity pattern and intensity similar to the observed data. Two to three outer loops with 50-100 iterations in each loop were needed to obtain a converged 3-D analysis of reflectivity, rainwater, snow, and graupel, including the melting layers with mixed-phase hydrometeors. It is shown that the deficiencies in the analysis using this operator, caused by the poor quality of the background fields and the use of the static background error covariance, can be partially resolved by using radar-retrieved hydrometeors in a preprocessing step and tuning the spatial correlation length scales of the background errors. The direct radar reflectivity assimilation using Radar-Var also improved the short-term (2-5 h) precipitation forecasts compared to those of the experiment without DA.

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Section: Contribution From Rainwatermentioning

confidence: 99%

mentioning

confidence: 99%

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A radar reflectivity operator with ice-phase hydrometeors for variational data assimilation (version 1.0) and its evaluation with real radar data

Wang

Liu

2019

Geosci. Model Dev.

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show abstract

“…After performing the integration, the contribution from pure rainwater, Z r , is written in a simple form (Zhang et al, 2001;Posselt et al, 2015;Kawabata et al, 2018) as follows:…”

Section: Contribution From Rainwatermentioning

confidence: 99%

A Radar Reflectivity Operator with Ice-Phase Hydrometeors for Variational Data Assimilation (RadZIceVarv1.0) and Its Evaluation with Real Radar Data

Wang¹,

Liu²

2019

Preprint

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Abstract. A reflectivity forward operator and its associated tangent linear and adjoint operators (together named RadZIceVar) were developed for variational data assimilation (DA). RadZIceVar can analyze both rainwater and ice-phase species (snow and graupel) by directly assimilating radar reflectivity observations. The results of three-dimensional variational (3DVAR) DA experiments with a 3 km grid mesh setting of the Weather Research and Forecasting (WRF) model showed that RadZIceVar was effective at producing an analysis of reflectivity pattern and intensity similar to the observed data. Two to three outer loops with 50–100 iterations in each loop were needed to obtain a converged 3D analysis of rainwater, snow, and graupel, including the melting layers with mixed-phase hydrometeors. The deficiencies in the analysis using this operator could be caused by the poor quality of the background fields and the use of the static background error covariance, and these issues can be partially resolved by using radar-retrieved hydrometeors in a preprocessing step and tuning the spatial correlation length scales of the background errors. The direct radar reflectivity assimilation using RadZIceVar also improved the short-term (2 h–5 h) precipitation forecasts compared to those of the experiment without DA.

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“…(2011) and Kawabata et al . (2018) demonstrated the benefit of directly assimilating reflectivity and the dual‐polarimetric parameter.…”

Section: Introductionmentioning

confidence: 99%

One‐dimensional maximum‐likelihood estimation for spaceborne precipitation radar data assimilation

Ikuta

Okamoto

Kubota

2020

Quart J Royal Meteoro Soc

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Spaceborne precipitation radar such as Global Precipitation Measurement (GPM)/dual‐frequency precipitation radar (DPR) provides valuable observations of precipitation systems in three dimensions. Assimilation of GPM/DPR data is becoming an important technique for improving the accuracy of forecasting to complement scarce ground‐based observations. This study presents a new, one‐dimensional maximum‐likelihood estimation (1D‐MLE) method developed by the authors that enables the estimation of relative humidity profiles according to a non‐Gaussian probability density function. By assimilating the estimated relative humidity profiles using a four‐dimensional variational (4D‐Var) method, mesoscale precipitation forecasts by the Japan Meteorological Agency (JMA) have been considerably improved. The displacement of forecast precipitation during a severe weather event, in particular, is improved significantly. It was found that forecasting accuracy was maintained for a narrow GPM/DPR swath and low revisit frequency by repeating the assimilation–forecast cycle. Since the effectiveness was confirmed, the JMA began assimilating GPM/DPR data using the 1D‐MLE approach from March 2016.

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Observational operators for dual polarimetric radars in variational data assimilation systems (PolRad VAR v1.0)

Cited by 16 publications

References 38 publications

A radar reflectivity operator with ice-phase hydrometeors for variational data assimilation (version 1.0) and its evaluation with real radar data

A radar reflectivity operator with ice-phase hydrometeors for variational data assimilation (version 1.0) and its evaluation with real radar data

A Radar Reflectivity Operator with Ice-Phase Hydrometeors for Variational Data Assimilation (RadZIceVarv1.0) and Its Evaluation with Real Radar Data

One‐dimensional maximum‐likelihood estimation for spaceborne precipitation radar data assimilation

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