Forecasting weather radar propagation conditions |
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| Authors: | J Bech B Codina J Lorente |
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| Institution: | (1) Catalan Meteorological Service, Barcelona, Spain;(2) Departament d’Astronomia i Meteorologia, Universitat de Barcelona, Barcelona, Spain |
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| Abstract: | Summary The increasing use of weather radar quantitative precipitation estimates, particularly in automatic applications such as operational
hydrometeorological modelling or assimilation in numerical weather prediction (NWP) models, has promoted the development of
quality control procedures on radar data.
Anomalous propagation (AP) of the radar beam due to deviation from the standard refractivity vertical profile, is one of the
factors that may affect seriously the quality of radar observations because of the increase in quantity and intensity of non-precipitating
clutter echoes and consequent contamination of the estimated rainfall field. Another undesired effect of AP is the change
in the expected radar echo height, which may be relevant when correcting for beam blockage in radar rainfall estimation in
complex terrain. The aim of this paper is to study the use of NWP mesoscale forecasts to predict and monitor AP events.
A nested 15-km grid resolution version of the MASS model has been used to retrieve refractivity profiles in the coastal area
of Barcelona, near a weather radar and a radiosonde station. Using the refractivity profiles two different magnitudes were
computed: the vertical refractivity profile of the lowest 1000 m layer and a ducting index which describes the existence and
intensity of the most super-refractive layer contained in the lowest 3-km layer. A comparison between model forecasts and
radiosonde diagnostics during a six-month period showed that the model tended to underestimate the degree of super-refraction,
with a bias of 4 km−1 and RMSE of 11 km−1 in the 1-km vertical refractivity gradient. Further analysis of the data showed that a combination of previous observations
and forecasts allowed to produce modified forecasts improving the original direct model output, decreasing substantially the
bias, reducing the RMSE by 20% and improving the skill by 40%, beating also radiosonde observations persistence. |
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