Developments in Radar Refractivity Retrieval John Nicol 1, Anthony Illingworth 1, Kim Bartholomew 1, Tim Darlington 2 Malcolm Kitchen 2, Jon Eastment 3 and Owain Davies 3 1 University of Reading 2 UK Met Office 3 Chilbolton Observatory, RAL Joint 8th COPS Workshop and CSIP Meeting th-28th October 2009, Madingley Hall, Cambridge, UK.

Radar Refractivity Retrieval Background A technique to derive maps of refractive index changes from radar data was first demonstrated using an S-band (10 cm) weather radar in Fabry et. al. (1997) For stationary targets (ground clutter), phase changes are typically dominated by changes in the refractive index (n) of the air near the surface Refractivity N=(n-1)10 6 Refractivity changes (  N) are proportional to the phase change gradient with respect to range Refractivity changes are very closely linked to changes in humidity Changes in the spatial distribution of near-surface water vapour are not currently well-observed by other means Near-surface fields of refractivity (humidity) changes may benefit QPE and Nowcasting through data assimilation in Numerical Weather Prediction models Motivation

First application of radar refractivity in the UK ACROBAT – Chilbolton CSIP L-band wavelength (23cm) Designed for detecting weak meteorological echoes (Bragg scatter) Coded pulse greater sensitivity for low-power radars  range artefacts from intense returns Radar refractivity estimates were good at times, though often inaccurate when changes were large… CSIP IOP 8, 13/07/2005 Sea-breeze passes Chilbolton after 15:00 RH increases 10% across sea-breeze front

Digital terrain model The Operational Weather Radar Network in the UK Magnetron transmitters  prone to frequency drift (due to ambient temperature changes in the receiver cabin) measure transmitted frequency in real-time C-band wavelength (5 cm)  phase changes are more sensitive to target motion at shorter wavelengths Testing and development of radar refractivity retrieval at Cobbacombe, Devon Quantitative evaluation using synoptic station measurements, N(T,P,e) x x

Application (deriving hourly refractivity changes) 2.Identify correlated range-gates Independent range-gates → refractivity changes Highly-correlated range-gates → transmitted frequency changes  Magnetron transmitters (  N freq = -  f T /f T ) Klystron transmitters (  N freq = 0) 1. Identify stationary targets Target motion affects the phase during the measurement process Phase Quality Indicator (PQI) is recorded for every ray (≈ 44 pulses/deg.) at each range gate PQI = Incoherent signal power Total signal power Optimum threshold ≈ -5 dB   dBZ

Application cont. (deriving hourly refractivity changes) 3. Correct local oscillator frequency changes (e.g. STALO frequency changes) Local oscillator frequency changes cause a range dependent phase change (  = 4  f LO d/c) 4. Smooth phase change field Smoothing function Inverse-distance squared with base length = 1.5km (range) x 4 km (azimuth) 5. Calculate the phase change gradient w.r.t. range for all target pairs local mean → refractivity change local std. dev. → error estimate NN NN

Comparison of hourly refractivity changes with synoptic station obs. March 2008  N rad  N syn  N rad  N syn NN NN rms (  N rad -  N syn ) June 2008 Correlation between  N freq and  N rad Station 1 2 (1 & 2) with elimination of correlated range-gates without elimination of correlated range-gates correlation 0.60 slope 0.63 correlation 0.51 slope 0.48 rms difference ≈ error estimate / 2 Synoptic stations show greater variability (‘point’ measurements vs. area average [4km]) Refractivity error evaluation Importance of eliminating correlated range-gates March 2008June 2008 Calculate rms difference as a function of the error estimate

Met Office Unified Model comparisons Case study: Isolated convection 07/06/2008 Model refractivity Radar rainfall Synoptic station refractivity Correlation with synoptic observations of refractivity. Station 1 2 Radar UM 4km UM 12km Temperature and pressure often well-captured in the model … but not humidity

Initial comparisons between radar refractivity retrieval and the Met Office Unified Model output suggest that radar refractivity retrieval may improve the representation of humidity in the model (not well-captured at present) Radar refractivity error estimates should provide useful constraints for data assimilation Future work will aim to evaluate the benefit of assimilating radar refractivity measurements in the Unified Model regarding the initiation/suppression of convection Summary and Future Work (1)

Summary and Future Work (2) Radar refractivity retrieval is possible with magnetron radars and has highlighted the importance of eliminating correlated range-gates, which bias refractivity change estimates. Klystron radars will be biased towards  N=0 ! Inconsistent results from CSIP may well be due to this effect, in conjunction with the pulse-coding artefacts. Data soon to be re-analysed! Newly-developed theory suggests exact target location could be retrieved using frequency hopping from pulse-to-pulse. Use exact spacing rather than range-gate spacing to calculate phase change gradients. To be tested on ACROBAT (no pulse coding).

Thanks for your attention!

Relation between refractivity and meteorological variables TotalDensity (dry) term Wet term  Refractivity changes are dominated by humidity changes in warm conditions could be caused by any of the following

Stockland Hill TV Mast (235m at 30km) azimuthrange dBZ phase Single targets may contaminate many range gates

B j k Frequency changes from dominant ‘point’ clutter targets When a single target dominates the returned signal across adjacent range gates, the phase change gradient with respect to range will be proportional to the change in transmitted frequency This requires that the reference frequency is either corrected for or remains unchanged By averaging the estimates from such targets, changes in the transmitted frequency can be accurately determined (black) In addition, the transmitted frequency is independently measured in real-time by sampling the transmitted pulse (red) Comparisons between these two estimates have confirmed that the measured transmitted frequency and the recorded reference frequency are accurate to better than 1kHz