An Integrated Approach to Weather Radar Calibration and Monitoring Using Ground Clutter and Satellite Comparisons

Abstract

The stability and accuracy of weather radar reflectivity calibration are imperative for quantitative applications, such as rainfall estimation, severe weather monitoring and nowcasting, and assimilation in numerical weather prediction models. Various radar calibration and monitoring techniques have been developed, but only recently have integrated approaches been proposed, that is, using different calibration techniques in combination. In this paper the following three techniques are used: 1) ground clutter monitoring, 2) comparisons with spaceborne radars, and 3) the self-consistency of polarimetric variables. These techniques are applied to a C-band polarimetric radar (CPOL) located in the Australian tropics since 1998. The ground clutter monitoring technique is applied to each radar volumetric scan and provides a means to reliably detect changes in calibration, relative to a baseline. It is remarkably stable to within a standard deviation of 0.1 dB. To obtain an absolute calibration value, CPOL observations are compared to spaceborne radars on board TRMM and GPM using a volume-matching technique. Using an iterative procedure and stable calibration periods identified by the ground echoes technique, we improve the accuracy of this technique to about 1 dB. Finally, we review the self-consistency technique and constrain its assumptions using results from the hybrid TRMM–GPM and ground echo technique. Small changes in the self-consistency parameterization can lead to 5 dB of variation in the reflectivity calibration. We find that the drop-shape model of Brandes et al. with a standard deviation of the canting angle of 12° best matches our dataset.

The first Met Office Unified Model–JULES Regional Atmosphere and Land configuration, RAL1

“…Land-sea mask IGBP; Loveland et al (2000) Used for UKV/MOGREPS-UK CCI; Hartley et al (2017) CCI mask lacking in inland lakes definition Mean/sub-grid orography DTED 1 km; Used for UKV/MOGREPS-UK GLOBE 30 ; Hastings et al (1999) Fields filtered before use SRTM; Bunce et al (1996) Shuttle Radar Topography Mission; mean orography only; available up to 60 • north Land usage IGBP; Loveland et al (2000) Mapped to nine tile types ITE; Bunce et al (1996) UK only CCI; Hartley et al (2017) European Space Agency Land Cover Climate Change Initiative Soil properties HWSD; Nachtergaele et al (2008) Three datasets blended via optimal interpolation STATSGO; Miller and White (1998) We document a list of acronyms in Table A2. Numerical weather prediction RAL Regional Atmosphere and Land RAL0…”

Nine-Year Systematic Evaluation of the GPM and TRMM Precipitation Products in the Shuaishui River Basin in East-Central China

“…Availability of traditional ground observations has been limited because of the inadequate and uneven distribution of rain gauges, especially in developing countries, mountainous and remote areas, and over oceans [6]. On the other hand, although weather radar products can provide rainfall observations over a wide region [7], they are subjected to both random and systematic errors [8][9][10][11]. Random errors could arise from the sub-grid horizontal and vertical variability of rainfall and the noise of the radar hardware system, while systematic errors may originate from sources such as drifts in radar calibration constant, systematic variations in the reflectivity-rain-rate relationship, and strong gradients in the reflectivity profile [12].…”

UNRAVEL: A Robust Modular Velocity Dealiasing Technique for Doppler Radar

“…UNRAVEL is open source, and is released as part of the Python Package Index (named unravel). The exact version of the UNRAVEL code presented herein is available here: Louf and Jackson (2020). Note that we are currently working on the integration of UNRAVEL in the Py-ART (Helmus and Collis 2016).…”