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M. Lindskog
,
K. Salonen
,
H. Järvinen
, and
D. B. Michelson

Abstract

A Doppler radar wind data assimilation system has been developed for the three-dimensional variational data assimilation (3DVAR) scheme of the High Resolution Limited Area Model (HIRLAM). Radar wind observations can be input for the multivariate HIRLAM 3DVAR either as radial wind superobservations (SOs) or as vertical profiles of horizontal wind obtained with the velocity–azimuth display (VAD) technique. The radar wind data handling system, including data processing, quality control, and observation operators for the 3DVAR, are described and evaluated. Background error standard deviation (σ b) in observation space for wind and radial wind have been estimated by the so-called randomization method. The derived values of σ b are used in the quality control of observations and also in the assignment of radar wind observation error standard deviations (σ o). Parallel data assimilation and forecast experiments confirm reasonably tuned error statistics and indicate a small positive impact of radar wind data on the verification scores, for both inputs.

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M. Heistermann
,
S. Collis
,
M. J. Dixon
,
S. Giangrande
,
J. J. Helmus
,
B. Kelley
,
J. Koistinen
,
D. B. Michelson
,
M. Peura
,
T. Pfaff
, and
D. B. Wolff

Abstract

Weather radar analysis has become increasingly sophisticated over the past 50 years, and efforts to keep software up to date have generally lagged behind the needs of the users. We argue that progress has been impeded by the fact that software has not been developed and shared as a community.

Recently, the situation has been changing. In this paper, the developers of a number of open-source software (OSS) projects highlight the potential of OSS to advance radar-related research. We argue that the community-based development of OSS holds the potential to reduce duplication of efforts and to create transparency in implemented algorithms while improving the quality and scope of the software. We also conclude that there is sufficiently mature technology to support collaboration across different software projects. This could allow for consolidation toward a set of interoperable software platforms, each designed to accommodate very specific user requirements.

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M. Heistermann
,
S. Collis
,
M. J. Dixon
,
J. J. Helmus
,
A. Henja
,
D. B. Michelson
, and
Thomas Pfaff

Abstract

In a recent BAMS article, it is argued that community-based Open Source Software (OSS) could foster scientific progress in weather radar research, and make weather radar software more affordable, flexible, transparent, sustainable, and interoperable.

Nevertheless, it can be challenging for potential developers and users to realize these benefits: tools are often cumbersome to install; different operating systems may have particular issues, or may not be supported at all; and many tools have steep learning curves.

To overcome some of these barriers, we present an open, community-based virtual machine (VM). This VM can be run on any operating system, and guarantees reproducibility of results across platforms. It contains a suite of independent OSS weather radar tools (BALTRAD, Py-ART, wradlib, RSL, and Radx), and a scientific Python stack. Furthermore, it features a suite of recipes that work out of the box and provide guidance on how to use the different OSS tools alone and together. The code to build the VM from source is hosted on GitHub, which allows the VM to grow with its community.

We argue that the VM presents another step toward Open (Weather Radar) Science. It can be used as a quick way to get started, for teaching, or for benchmarking and combining different tools. It can foster the idea of reproducible research in scientific publishing. Being scalable and extendable, it might even allow for real-time data processing.

We expect the VM to catalyze progress toward interoperability, and to lower the barrier for new users and developers, thus extending the weather radar community and user base.

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E. Raschke
,
J. Meywerk
,
K. Warrach
,
U. Andrea
,
S. Bergström
,
F. Beyrich
,
F. Bosveld
,
K. Bumke
,
C. Fortelius
,
L. P. Graham
,
S.-E. Gryning
,
S. Halldin
,
L. Hasse
,
M. Heikinheimo
,
H.-J. Isemer
,
D. Jacob
,
I. Jauja
,
K.-G. Karlsson
,
S. Keevallik
,
J. Koistinen
,
A. van Lammeren
,
U. Lass
,
J. Launianen
,
A. Lehmann
,
B. Liljebladh
,
M. Lobmeyr
,
W. Matthäus
,
T. Mengelkamp
,
D. B. Michelson
,
J. Napiórkowski
,
A. Omstedt
,
J. Piechura
,
B. Rockel
,
F. Rubel
,
E. Ruprecht
,
A.-S. Smedman
, and
A. Stigebrandt

The Baltic Sea Experiment (BALTEX) is one of the five continental-scale experiments of the Global Energy and Water Cycle Experiment (GEWEX). More than 50 research groups from 14 European countries are participating in this project to measure and model the energy and water cycle over the large drainage basin of the Baltic Sea in northern Europe. BALTEX aims to provide a better understanding of the processes of the climate system and to improve and to validate the water cycle in regional numerical models for weather forecasting and climate studies. A major effort is undertaken to couple interactively the atmosphere with the vegetated continental surfaces and the Baltic Sea including its sea ice. The intensive observational and modeling phase BRIDGE, which is a contribution to the Coordinated Enhanced Observing Period of GEWEX, will provide enhanced datasets for the period October 1999–February 2002 to validate numerical models and satellite products. Major achievements have been obtained in an improved understanding of related exchange processes. For the first time an interactive atmosphere–ocean–land surface model for the Baltic Sea was tested. This paper reports on major activities and some results.

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