The following section introduces opportunities for future work which develop from this thesis, the first three of which are aimed more towards further improving the processing chain of the radar, while the remaining two have a more outward focus on other aspects of radar hydrometeorology. In practice each method can be beneficially applied to both radar processing and atmospheric science providing improvements in both QPE and process understanding.
8.2.1 Merging QC reflectivity with radar filtered reflectivity
A potential improvement to the radar processing chain would be the inclusion of data from the radar filtered reflectivity product where appropriate. Chapter 4 showed the radar filtered reflectivity had two issues, the first being an under removal of ground
Chapter 8. Synthesis 171
clutter returns in dry conditions and the second being removal of rainfall along the zero velocity isodop. As ground clutter signals have been identified using the fuzzy classifier one potential approach would be to derive a fixed clutter map based on the reflectivity data, the reflectivity signal for which would be set to invalid where the classifier identifies ground clutter but to the radar filtered reflectivity where a rainfall echo is identified. This approach would improve the reflectivity measurements in mixed echo type conditions where both a ground clutter and rainfall echo are combined. A further extension of this could then be using the radar filtered reflectivity in conditions where the fuzzy classifier has identified a ground clutter echo outside of the clutter map, which could improve performance when meteorological echoes have a similar signature to ground clutter echoes, such as in narrow convective cores with high reflectivity and rapid spatial variability.
8.2.2 Expansion of the fuzzy logic scheme
The expansion of the fuzzy logic classification scheme to include different hydrometeor types including graupel, wet snow, hail and ice will lead to improvements in false classifi- cation during quality control and has the potential to improve rainfall estimates through selective combination of the moments used depending on the main echo signatures iden- tified within a range gate. Existing classification schemes at X-band exist in the USA based on the theoretical simulation of dual polarisation moments using T-matrix scat- tering simulations (Schneider et al., 2013; Dolan and Rutledge, 2009). Modification of the membership functions in these schemes to fit the fuzzy logic framework defined here is one possible route to an expanded classification scheme while another option is to empirically define new functions based on the observations obtained during COPE and the additional field campaigns listed in Appendix B. Given many hydrometeor classifi- cation schemes require a temperature profile to determine the difference between liquid and solid phase precipitation there may be the need to incorporate external data into the processing chain. Alternatively melting layer identification techniques could be applied to the radar data, particularly to the QVP analysis noted later in this Chapter, to de- fine the 0◦C height which is required by these schemes. The introduction of hydrometeor
classification to the fuzzy logic scheme also has the potential to improve atmospheric pro- cess studies through the comparison of radar derived hydrometeor type with modelled hydrometeors and aircraft observations.
8.2.3 Estimating KDP with increased spatial resolution
The current radar estimates of specific differential phase shift have low spatial resolution as a result of the smoothing undertaken in their production. This low resolution leads to the underestimation of peak rainfall when using KDP and offsets between regions when
merging multiple rainfall estimates together. Alternative schemes exist for the calculation of KDP which advocate a variable length window for calculation of the gradient of ΦDP
which decreases (increases) in length when reflectivity is high (low) and these could be tested as a first alternative for KDP estimation with the mobile radar (Brandes et al.,
2001). Increasing the spatial resolution ofKDP will improve rainfall estimates and allow
better process studies in convective cells where the gradients currently calculated are unrepresentative of the rates of change occurring across a small distance. Another option to explore is the modification of the new smoothing methodology for ΦDP developed in this thesis, which currently uses two window lengths. Dynamic adjustment of these filter lengths based on reflectivity values could lead to improved KDP estimates in light
rainfall through an extension of the filtering window in these regions which will reduce the uncertainty in low values ofKDP.
8.2.4 QVP
Quasi vertical profiles (QVP) of radar variables are an emerging method of visualising dual polarisation radar data which use azimuthal averaging to reduce the measurement uncertainty of dual polarisation moments and create a representation of the vertical structure of stratiform storms (Kumjian et al., 2016; Ryzhkov et al., 2016; Schrom and Kumjian, 2016). During Chapter 7 QVP of reflectivity were used to successfully identify the cause of radar underestimation of ground rainfall during a period of low intensity post frontal precipitation, though not to correct explicitly for the error due to local variations. The application of QVP to case studies to improve our understanding of cloud micro- physics has great potential as a future application of the mobile radar, while QVP will
Chapter 8. Synthesis 173
also be useful for numerical weather model evaluation. Figure 8.2 provides an example of QVP showing the changing height profile of three dual polarisation moments (horizontal reflectivity, differential reflectivity and the co-polar correlation coefficient) along with a true vertical profile of Doppler velocity from the recent radar deployment during RAINS (see Appendix B). Though none of the radar parameters have been corrected there are some clear signals in the height profiles beyond the obvious delineation of the melting layer including the change in differential reflectivity above the melting layer, which ap- pears to be linked to echo top height and also decreases of the correlation coefficient within the melting layer and broadening of its structure linked to the highest sub melting level reflectivity. The investigation of these observations and others from COPE, RAINS and other radar field campaigns will be a fascinating future application of the radar data processing described in this study and will link strongly to the hydrometeor classification work described earlier in this chapter.
8.2.5 Hydrological modelling and uncertainty
An original intention of this thesis was to incorporate dual polarisation radar estimates of rainfall into hydrological models to develop an understanding of the uncertainty in flow simulations driven by radar input. To undertake this research data from the Bealsmill river gauge (Section A.3) was obtained for the COPE field campaign, along with river level and UKMO network radar data from the preceding summers to allow calibration of a rainfall-runoff model. Unfortunately the nature of the mobile radar deployment site in Cornwall prevented continuous operation of the radar, making river runoff modelling impossible without splicing together data records and the summer was particularly dry, leading to only a single example of elevated river levels at the gauging site over the course of the field campaign. Preliminary research presented at international conferences suggested that the parametric uncertainty within hydrological models is as significant as the uncertainty in radar rainfall estimates and that further connected studies combining the two sources of uncertainty would be beneficial (Dufton et al., 2014; Dufton and Collier, 2013). Although these studies are not possible using the data from the COPE field campaign, the data obtained during the RAINS campaign (Appendix B) in 2016 will provide an excellent opportunity to revisit this research in the future.
Figure 8.2: Quasi vertical profiles of reflectivity (top panel), differential reflectivity
(second panel), correlation coefficient (third panel) and true vertical profile of radial velocity (bottom panel) from 24 July 2016 during the RAINS field campaign. Data from a 20◦elevation PPI scan has been averaged azimuthally and projected onto a vertical plane to represent the vertical structure of the precipitation system. Vertical data is an
average of a bird bath 90◦scan obtained immediately after each PPI volume.