Introduction

Motivation

Chilbolton Observatory is a world-class radar facility for meteorological research. However, a substantial barrier to more widespread use of Chilbolton radar data is the fact that it is not routinely available in a standard data format with all of the processing issues, about which the the average meteorological user knows nothing, taken care of. This is particularly true in the case of cloud radar data, for which it is essential to perform accurate `noise subtraction' if anything like the maximum sensitivity of the instrument is to be realised. This collection of utilities provides the ability to convert data stored in the in-house Chilbolton radar format to a form that is easy to read using standard data-analysis packages (such as matlab and IDL), while also doing all the necessary processing. It is also possible to produce quicklooks of the processed data.

Features

The processing options available to the programs chil2a, chil2nc, chilplot and loadchil are as follows:

General

• Averaging over gates and rays; this is particularly useful for cloud radar data which is recorded at a much higher temporal resolution (around 1 second) than is often required (around 1 minute), and affords greater sensitivity. Z is averaged in a true linear sense.
• Correction of the various parameters using specified calibration values.
• (Re)calculation of range calibration using a different range offset. This is typically used with cloud radar data for which the recording starts before the pulse has gone out, and is now not range calibrated at all.
• Ability to read raw (little endian) data files. This enables near real-time radar images to be created for use on the web.

Scanning data

• Subtraction of the noise contributions from Z, ZDR and LDR using the noise figures of the H, V and cross-polar channels. This is essential if these parameters are to be used at low signal to noise ratios, and also sets the `clear-sky' regions to no signal.
• Simple rejection of ground clutter using LDR.

Cloud data

• Output of data by day rather than by raster.
• Noise subtraction using gates from the top of the ray to characterise the magnitude of the noise.
• Noise subtraction but taking account of interference by an oscillator in the received rays by using the gates at the top of the ray to characterise the shape of the oscillation. If this is not done for data from the Galileo radar then anomalous lines of pixel-width cloud are apparent in the processed data.
• Removal of vertically-isolated pixels: cleans up most remaining anomalous lines.
• Simple ground clutter removal using v and sigma_v to find a stationary signal with a narrow spectral width.
• Rejection of `range-glitched' rays, which sometimes occur with the Galileo radar when the transmitter and receiver get out of sync.
• Rejection of rays when the radar is recording in cloud mode but is not vertically pointing.