Long time radar data series processing using OPERA ODIM

We present the results of a feasibility study on the possibility of using the OPERA data information model (ODIM) with HDF5 file format implementation [Michelson et. al. 2021] for reprocessing long-term weather radar data series in the context of a small national meteorological service such as the Slovak Hydrometeorological Institute (SHMU). 

ODIM was developed during the Operational Programme on the Exchange of Weather Radar Information (OPERA) under the umbrella of the European Meteorological Services Network, EIG EUMETNET [Saltikoff et al. 2019] to support the exchange of volume radar data between meteorological services and different radar systems.

Work was performed in 3 consequent steps:

1. Radar archive consolidation

SHMU has been working with radar data since 1965 [Podhorský & Guba 2014], but the level of equipment available didn't allow us to digitally archive such a large amount of data. We were able to extract archived radar from 1998 to the present. Data from earlier periods were archived on analogue media (e.g. paper drawings, photographs) or on special tapes that we cannot read today. More than 50 old data tapes and more than 500 CDs and DVDs have been extracted. The most recent period was stored in the robotic tape library. We will discuss all the problems we encountered during this step.

2. Conversion to ODIM hdf5 files

From the archive we extracted data from 4 different radar systems: MRL-5, EEC DWSR92C, Radtec RDR-250GCDP with Gamic signal processor and Selex Meteor 735 CDP. 5 different converters were prepared. The most interesting was the converter for MRL-5 data. It only stores raw data, radar equation has to be applied, noise figure subtracted. The scan was not clearly defined, volumes store all data from start of measurement to end, sampled in any position of the antenna, and we got uncorrected reflectivity for S- and X-band channel, because MRL-5 was classic non-Doppler radar and clutter has to be filtered in processing.

3. Processing

For processing, we used "home-made" qRad software running on hpc. It is capable of generating any standard product and making a common composite based on 11+ different quality indices. The clutter map was calculated from the previous 2-3 week period. We had to solve problems with temporal and spatial variability of the input data. The scan repetition time had values of 30, 15, 7.5, 10 and 5 min time. The number of radars varied between 0 and 4. We decided to use only those dates when at least 2 radars were available and covered the whole of Slovakia. As a reference product, the composite of CAPPI 3km was chosen, which provides uniform data for all radars over whole Slovakia. Maps of various statistical variables were calculated from CAPPI 3km composites.