Parameterized Forward Operators for Simulation and Assimilation of Polarimetrie Radar Data with Numerical Weather Predictions

AbstractMany weather radar networks in the world have now provided polarimetric radar data (PRD) that have the potential to improve our understanding of cloud and precipitation microphysics, and numerical weather prediction (NWP). To realize this potential, an accurate and efficient set of polarimetric observation operators are needed to simulate and assimilate the PRD with an NWP model for an accurate analysis of the model state variables. For this purpose, a set of parameterized observation operators are developed to simulate and assimilate polarimetric radar data from NWP model-predicted hydrometeor mixing ratios and number concentrations of rain, snow, hail, and graupel. The polarimetric radar variables are calculated based on the T-matrix calculation of wave scattering and integrations of the scattering weighted by the particle size distribution. The calculated polarimetric variables are then fitted to simple functions of water content and volume-weighted mean diameter of the hydrometeor particle size distribution. The parameterized PRD operators are applied to an ideal case and a real case predicted by the Weather Research and Forecasting (WRF) model to have simulated PRD, which are compared with existing operators and real observations to show their validity and applicability. The new PRD operators use less than one percent of the computing time of the old operators to complete the same simulations, making it efficient in PRD simulation and assimilation usage.

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Evaluation of convective cloud microphysics in numerical weather predictionmodel with dual-wavelength polarimetric radar observations

10.5194/dach2022-281 ◽

2021 ◽

Author(s):

Gregor Köcher ◽

Florian Ewald ◽

Martin Hagen ◽

Christoph Knote ◽

Eleni Tetoni ◽

...

Keyword(s):

Particle Size ◽

Weather Prediction ◽

Cloud Microphysics ◽

Dual Wavelength ◽

Size Distributions ◽

Polarimetric Radar ◽

Particle Size Distributions ◽

Numerical Weather ◽

High Impact Weather ◽

Radar Signals

The representation of microphysical processes in numerical weather prediction models remains a main source of uncertainty until today. To evaluate the influence of cloud microphysics parameterizations on numerical weather prediction, a convection permitting regional weather model setup has been established using 5 different microphysics schemes of varying complexity (double-moment, spectral bin, particle property prediction (P3)). A polarimetric radar forward operator (CR-SIM) has been applied to simulate radar signals consistent with the simulated particles. The performance of the microphysics schemes is analyzed through a statistical comparison of the simulated radar signals to radar measurements on a dataset of 30 convection days. The observational data basis is provided by two polarimetric research radar systems in the area of Munich, Germany, at C- and Ka-band frequencies and a complementary third polarimetric C-band radar operated by the German Weather Service. By measuring at two different frequencies, the dual-wavelength ratio is derived that facilitates the investigation of the particle size evolution. Polarimetric radars provide in-cloud information about hydrometeor type and asphericity by measuring, e.g., the differential reflectivity ZDR. Within the DFG Priority Programme 2115 PROM, we compare the simulated polarimetric and dual-wavelength radar signals with radar observations of convective clouds. Deviations are found between the schemes and observations in ice and liquid phase, related to the treatment of particle size distributions. Apart from the P3 scheme, simulated reflectivities in the ice phase are too high. Dual-wavelength signatures demonstrate issues of most schemes to correctly represent ice particle size distributions. Comparison of polarimetric radar signatures reveal issues of all schemes except the spectral bin scheme to correctly represent rain particle size distributions. The polarimetric information is further exploited by applying a hydrometeor classification algorithm to obtain dominant hydrometeor classes. By comparing the simulated and observed distribution of hydrometeors, as well as the frequency, intensity and area of high impact weather situations (e.g., hail or heavy convective precipitation), the influence of cloud microphysics on the ability to correctly predict high impact weather situations is examined.

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A Microphysical Bulk Formulation Based on Scaling Normalization of the Particle Size Distribution. Part I: Description

Journal of the Atmospheric Sciences ◽

10.1175/jas3620.1 ◽

2005 ◽

Vol 62 (12) ◽

pp. 4206-4221 ◽

Cited By ~ 27

Author(s):

Wanda Szyrmer ◽

Stéphane Laroche ◽

Isztar Zawadzki

Keyword(s):

Particle Size ◽

High Resolution ◽

Particle Size Distribution ◽

Size Distribution ◽

Numerical Models ◽

Radar Data ◽

Retrieval Algorithm ◽

Microphysical Processes ◽

The Moment ◽

Microphysical Parameterization

Abstract The authors address the problem of optimization of the microphysical information extracted from a simulation system composed of high-resolution numerical models and multiparameter radar data or other available measurements. As a tool in the exploration of this question, a bulk microphysical scheme based on the general approach of scaling normalization of particle size distribution (PSD) is proposed. This approach does not rely on a particular functional form imposed on the PSD and naturally leads to power-law relationships between the PSD moments providing an accurate and compact PSD representation. To take into account the possible evolution of the shape/curvature of the distribution, ignored within standard one- and two-moment microphysical schemes, a new three-moment scheme based on the two-moment scaling normalization is proposed. The methodology of the moment retrieval included in the three-moment scheme can also be useful as a retrieval algorithm combining different remote sensing observations. The developed bulk microphysical scheme presents a unified formulation for microphysical parameterization using one, two, or three independent moments, suitable in the context of data assimilation. The effectiveness of the scheme with different combinations of independent moments is evaluated by comparison with a very high resolution spectral model within a 1D framework on representative microphysical processes: rain sedimentation and evaporation.

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Combined use of volume radar observations and high-resolution numerical weather predictions to estimate precipitation at the ground: methodology and proof of concept

Atmospheric Measurement Techniques ◽

10.5194/amt-12-5669-2019 ◽

2019 ◽

Vol 12 (10) ◽

pp. 5669-5684 ◽

Cited By ~ 1

Author(s):

Tony Le Bastard ◽

Olivier Caumont ◽

Nicolas Gaussiat ◽

Fatima Karbou

Keyword(s):

High Resolution ◽

Weather Prediction ◽

Ground Level ◽

Radar Data ◽

Horizontal Resolution ◽

Computationally Efficient ◽

New Approach ◽

Numerical Weather ◽

Simulated Precipitation ◽

Nwp Model

Abstract. The extrapolation of the precipitation to the ground from radar reflectivities measured at the beam altitude is one of the most delicate phases of radar data processing for producing quantitative precipitation estimations (QPEs) and remains a major scientific issue. In many operational meteorological services such as Météo-France, a vertical profile of reflectivity (VPR) correction is uniformly applied over a large part or the entire radar domain. This method is computationally efficient, and the overall bias induced by the bright band is most of the time well corrected. However, this way of proceeding is questionable in situations with high spatial and vertical variability of precipitation (during the passage of a cold front or in a complex terrain, for example). This study initiates from two statements: first, radars provide information on precipitation with a high spatio-temporal resolution but still require VPR corrections to extrapolate rain rates at the ground level. Second, the horizontal resolution of some numerical weather prediction (NWP) models is now comparable with the radar one, and their dynamical core and microphysics schemes allow the production of realistic simulations of VPRs. The present paper proposes a new approach to assess surface rainfall from radar reflectivity aloft by exploiting simulated VPRs and rainfall forecasts from the high-resolution NWP model AROME-NWC. To our knowledge, this is the first time that simulated precipitation profiles from an NWP model are used to derive radar QPEs. The implementation of the new method on two stratiform situations provided significant improvements on the hourly and 6 h accumulations compared to the operational QPEs, showing the relevance of this new approach.

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Combined use of volume radar observations and high-resolution numerical weather predictions to estimate precipitation at the ground: methodology and proof of concept

10.5194/amt-2019-166 ◽

2019 ◽

Cited By ~ 1

Author(s):

Tony Le Bastard ◽

Olivier Caumont ◽

Nicolas Gaussiat ◽

Fatima Karbou

Keyword(s):

High Resolution ◽

Weather Prediction ◽

Ground Level ◽

Radar Data ◽

Horizontal Resolution ◽

Computationally Efficient ◽

New Approach ◽

Numerical Weather ◽

Simulated Precipitation ◽

Nwp Model

Abstract. The extrapolation of the precipitation to the ground from radar reflectivities measured at the beam altitude is one of the most delicate phases of radar data processing for producing Quantitative Precipitation Estimations (QPEs) and remains a major scientific issue. In many operational meteorological services such as Météo-France, a Vertical Profile of Reflectivity (VPR) correction is uniformly applied over a large part or the entire radar domain. This method is computationally efficient and the overall bias induced by the bright band is most of the time well corrected. However, this way of proceeding is questionable in situations with high spatial and vertical variability of precipitation (during the passage of a cold front or in a complex terrain, for example). This study initiates from two statements: first, radars provide information on precipitation with a high spatio-temporal resolution but still require VPR corrections to extrapolate rain rates at the ground level. Second, the horizontal resolution of some Numerical Weather Prediction (NWP) models is now comparable with the radar one and their dynamical core and microphysics schemes allow to produce realistic simulations of VPRs. The present paper proposes a new approach to assess surface rainfall from radar reflectivity aloft by exploiting simulated VPRs and rainfall forecasts from the high resolution NWP model AROME-NWC. To our knowledge, this is the first time that simulated precipitation profiles from a NWP model are used to derive radar QPEs. The implementation of the new method on two stratiform situations provided significant improvements on the hourly and 6-h accumulations compared to the operational QPEs, showing the relevance of this new approach.

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