Doppler radar radial winds in HIRLAM. Part I: observation modelling and validation

Abstract An observation operator for Doppler radar radial wind measurements is developed further in this article, based on the earlier work and considerations of the measurement characteristic. The elementary observation operator treats radar observations as point measurements at pre-processed observation heights. Here, modelling of the radar pulse volume broadening in vertical and the radar pulse path bending due to refraction is included to improve the realism of the observation modelling. The operator is implemented into the High Resolution Limited Area Model (HIRLAM) limited area numerical weather prediction (NWP) system. A data set of circa 133 000 radial wind measurements is passively monitored against theHIRLAM six-hourly background values in a 1-month experiment.No data assimilation experiments are performed at this stage. A new finding is that the improved modelling reduces the mean observation minus background (OmB) vector wind difference at ranges below 55 km, and the standard deviation of the radial wind OmB difference at ranges over 25 km. In conclusion, a more accurate and still computationally feasible observation operator is developed. The companion paper (Part II) considers optimal super-observation processing of Doppler radar radial winds for HIRLAM, with general applicability in NWP.

[1]  Heikki Järvinen,et al.  Bias estimation of Doppler‐radar radial‐wind observations , 2007 .

[2]  P. Waldteufel,et al.  On the Analysis of Single-Doppler Radar Data. , 1979 .

[3]  Fathalla A. Rihan,et al.  Four-dimensional variational data assimilation for Doppler radar wind data , 2005 .

[4]  V. Ducrocq,et al.  First results of 1 D + 3 DVar assimilation of radar reflectivities , 2006 .

[5]  K. A. Browning,et al.  The Determination of Kinematic Properties of a Wind Field Using Doppler Radar , 1968 .

[6]  Juanzhen Sun,et al.  Dynamical and Microphysical Retrieval from Doppler Radar Observations Using a Cloud Model and Its Adjoint. Part I: Model Development and Simulated Data Experiments. , 1997 .

[7]  Tomas Landelius,et al.  Dealiasing of Doppler Radar Velocities Using a Torus Mapping , 2004 .

[8]  Henrik Vedel,et al.  Impact of Ground Based GPS Data on Numerical Weather Prediction , 2004 .

[9]  J. Marshall,et al.  THE DISTRIBUTION OF RAINDROPS WITH SIZE , 1948 .

[10]  Heikki Järvinen,et al.  Doppler Radar Wind Data Assimilation with HIRLAM 3DVAR , 2004 .

[11]  J. Rantakokko,et al.  Three-dimensional variational data assimilation for a limited area model : Part II: Observation handling and assimilation experiments , 2001 .

[12]  Asko Huuskonen,et al.  General incoherent scatter analysis and GUISDAP , 1996 .

[13]  Heikki Järvinen,et al.  An observation operator for ground-based GPS slant delays , 2006 .

[14]  Hajime Nakamura,et al.  Impacts of GPS-derived Water Vapor and Radial Wind Measured by Doppler Radar on Numerical Prediction of Precipitation , 2004 .

[15]  J. R. Probert-Jones,et al.  The radar equation in meteorology , 1962 .

[16]  Heikki Järvinen,et al.  Assimilation of radar radial winds in the HIRLAM 3D-var , 2000 .

[17]  B. Efron,et al.  A Leisurely Look at the Bootstrap, the Jackknife, and , 1983 .

[18]  2 B . 1 MODEL FOR DOPPLER RADAR RADIAL WINDS , .

[19]  Asko Huuskonen,et al.  A Proposed Solution to the Range–Doppler Dilemma of Weather Radar Measurements by Using the SMPRF Codes, Practical Results, and a Comparison with Operational Measurements , 2005 .

[20]  B. Efron.  The jackknife, the bootstrap, and other resampling plans , 1987 .

[21]  Christopher K. Wikle,et al.  Atmospheric Modeling, Data Assimilation, and Predictability , 2005, Technometrics.

[22]  Nils Gustafsson,et al.  Three-dimensional variational data assimilation for a limited area model : Part I: General formulation and the background error constraint , 2001 .

[23]  Conrad L. Ziegler,et al.  De-Aliasing First-Moment Doppler Estimates , 1977 .

[24]  December Per Und'en.  HIRLAM-5 Scientific Documentation , 2002 .