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

2018 ◽  
Vol 11 (6) ◽  
pp. 2493-2501 ◽  
Author(s):  
Takuya Kawabata ◽  
Thomas Schwitalla ◽  
Ahoro Adachi ◽  
Hans-Stefan Bauer ◽  
Volker Wulfmeyer ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Variational Data Assimilation ◽  
Japan Meteorological Agency ◽  
Weather Research And Forecasting ◽  
Differential Phase ◽  
Beam Bending ◽  
Adjoint Operators ◽  
Differential Reflectivity ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. We implemented two observational operators for dual polarimetric radars in two variational data assimilation systems: WRF Var, the Weather Research and Forecasting Model variational data assimilation system, and NHM-4DVAR, the nonhydrostatic variational data assimilation system for the Japan Meteorological Agency nonhydrostatic model. The operators consist of a space interpolator, two types of variable converters, and their linearized and transposed (adjoint) operators. The space interpolator takes account of the effects of radar-beam broadening in both the vertical and horizontal directions and climatological beam bending. The first variable converter emulates polarimetric parameters with model prognostic variables and includes attenuation effects, and the second one derives rainwater content from the observed polarimetric parameter (specific differential phase). We developed linearized and adjoint operators for the space interpolator and variable converters and then assessed whether the linearity of the linearized operators and the accuracy of the adjoint operators were good enough for implementation in variational systems. The results of a simple assimilation experiment showed good agreement between assimilation results and observations with respect to reflectivity and specific differential phase but not with respect to differential reflectivity.

scholarly journals Observational operators for dual polarimetric radars in variational data assimilation systems

2018 ◽  
Author(s):  
Takuya Kawabata ◽  
Thomas Schwitalla ◽  
Ahoro Adachi ◽  
Hans-Stefan Bauer ◽  
Volker Wulfmeyer ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Variational Data Assimilation ◽  
Japan Meteorological Agency ◽  
Weather Research And Forecasting ◽  
Differential Phase ◽  
Beam Bending ◽  
Adjoint Operators ◽  
Differential Reflectivity ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. We developed two observational operators for dual polarimetric radars and implemented them in two variational data assimilation systems: WRF Var, the Weather Research and Forecasting Model variational data assimilation system, and NHM-4DVAR, the nonhydrostatic variational data assimilation system for the Japan Meteorological Agency nonhydrostatic model. The operators consist of a space interpolator, two types of variable converters as well as their linearized and transposed (adjoint) operators. The space interpolator takes account of the effects of radar-beam broadening in both vertical and horizontal directions and climatological beam bending. The first variable converter emulates polarimetric parameters with model prognostic variables and includes attenuation effects, and the second one derives rainwater content from the observed polarimetric parameter (specific differential phase). We developed linearized and adjoint operators for the space interpolator and variable converters and then assessed whether the linearity of the linearized operators and the accuracy of the adjoint operators were good enough for implementation in variational systems. The results of a simple assimilation experiment showed good agreement between assimilation results and observations with respect to reflectivity and specific differential phase but not with respect to differential reflectivity.

scholarly journals A THREE-DIMENSIONAL AEROSOL VARIATIONAL DATA ASSIMILATION SYSTEM FOR AIRCRAFT AND SURFACE OBSERVATIONS

2018 ◽  
Vol XLII-3 ◽  
pp. 2215-2218
Author(s):  
Z. Zang ◽  
X. Pan ◽  
W. You ◽  
Y. Liang
Keyword(s):  
Data Assimilation ◽  
Local Time ◽  
Three Dimensional ◽  
Surface Concentration ◽  
Variational Data Assimilation ◽  
Weather Research And Forecasting ◽  
Aircraft Observations ◽  
Data Assimilation System ◽  
Surface Observations ◽  
Assimilation System

A three-dimensional variational data assimilation system is implemented within the Weather Research and Forecasting/Chemistry model, and the control variables consist of eight species of the Model for Simulation Aerosol Interactions and Chemistry scheme. In the experiments, the three-dimensional profiles of aircraft speciated observations and surface concentration observations acquired during the California Research at the Nexus of Air Quality and Climate Change field campaign are assimilated. The data assimilation experiments are performed at 02:00 local time 2 June 2010, assimilating surface observations at 02:00 and aircraft observations from 01:30 to 02:30 local time. The results show that the assimilation of both aircraft and surface observations improves the subsequent forecasts. The improved forecast skill resulting from the assimilation of the aircraft profiles persists a time longer than the assimilation of the surface observations, which suggests the necessity of vertical profile observations for extending aerosol forecasting time.

scholarly journals Simulation of Monsoon Depressions Using WRF-VAR: Impact of Different Background Error Statistics and Lateral Boundary Conditions

2014 ◽  
Vol 142 (10) ◽  
pp. 3586-3613 ◽  
Author(s):  
A. Routray ◽  
S. C. Kar ◽  
P. Mali ◽  
K. Sowjanya
Keyword(s):  
Data Assimilation ◽  
Boundary Conditions ◽  
Initial Conditions ◽  
Variational Data Assimilation ◽  
Error Statistics ◽  
Background Error ◽  
Single Observation ◽  
Monsoon Depressions ◽  
Data Assimilation System ◽  
Assimilation System

Abstract In a variational data assimilation system, background error statistics (BES) spread the influence of the observations in space and filter analysis increments through dynamic balance or statistical relationships. In a data-sparse region such as the Bay of Bengal, BES play an important role in defining the location and structure of monsoon depressions (MDs). In this study, the Indian-region-specific BES have been computed for the Weather Research and Forecasting (WRF) three-dimensional variational data assimilation system. A comparative study using single observation tests is carried out using the computed BES and global BES within the WRF system. Both sets of BES are used in the assimilation cycles and forecast runs for simulating the meteorological features associated with the MDs. Numerical experiments have been conducted to assess the relative impact of various BES in the analysis and simulations of the MDs. The results show that use of regional BES in the assimilation cycle has a positive impact on the prediction of the location, propagation, and development of rainbands associated with the MDs. The track errors of MDs are smaller when domain-specific BES are used in the assimilation cycle. Additional experiments have been conducted using data from the Interim European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-Interim) as initial and boundary conditions (IBCs) in the assimilation cycle. The results indicate that the use of domain-dependent BES and high-resolution ERA-I data as IBCs further improved the initial conditions for the model leading to better forecasts of the MDs.

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