The Convective Precipitation Experiment (COPE) was a joint U.K.–U.S. field campaign held during the summer of 2013 in the southwest peninsula of England, designed to study convective clouds that produce heavy rain leading to flash floods. The clouds form along convergence lines that develop regularly as a result of the topography. Major flash floods have occurred in the past, most famously at Boscastle in 2004. It has been suggested that much of the rain was produced by warm rain processes, similar to some flash floods that have occurred in the United States. The overarching goal of COPE is to improve quantitative convective precipitation forecasting by understanding the interactions of the cloud microphysics and dynamics and thereby to improve numerical weather prediction (NWP) model skill for forecasts of flash floods. Two research aircraft, the University of Wyoming King Air and the U.K. BAe 146, obtained detailed in situ and remote sensing measurements in, around, and below storms on several days. A new fast-scanning X-band dual-polarization Doppler radar made 360° volume scans over 10 elevation angles approximately every 5 min and was augmented by two Met Office C-band radars and the Chilbolton S-band radar. Detailed aerosol measurements were made on the aircraft and on the ground. This paper i) provides an overview of the COPE field campaign and the resulting dataset, ii) presents examples of heavy convective rainfall in clouds containing ice and also in relatively shallow clouds through the warm rain process alone, and iii) explains how COPE data will be used to improve high-resolution NWP models for operational use.

Abstract. In recent years, dual-polarisation Doppler X-band radars have become a widely used part of the atmospheric scientist's toolkit for examining cloud dynamics and microphysics and making quantitative precipitation estimates. This is especially true for research questions that require mobile radars. Here we describe the National Centre for Atmospheric Science (NCAS) mobile X-band dual-polarisation Doppler weather radar (NXPol) and the infrastructure used to deploy the radar and provide an overview of the technical specifications. It is the first radar of its kind in the UK. The NXPol is a Meteor 50DX manufactured by Selex-Gematronik (Selex ES GmbH), modified to operate with a larger 2.4 m diameter antenna that produces a 0.98∘ half-power beam width and without a radome. We provide an overview of the technical specifications of the NXPol with emphasis given to the description of the aspects of the infrastructure developed to deploy the radar as an autonomous observing facility in remote locations. To demonstrate the radar's capabilities, we also present examples of its use in three recent field campaigns and its ongoing observations at the NERC Facility for Atmospheric Radio Research (NFARR).

Analyses of the radar-observed structure and derived rainfall statistics of warm-season convection developing columns of enhanced positive differential reflectivity ZDR over England’s southwest peninsula are presented here. Previous observations of ZDR columns in developing cumulonimbus clouds over England were rare. The observations presented herein suggest otherwise, at least in the southwesterly winds over the peninsula. The results are the most extensive of their kind in the United Kingdom; the data were collected using the National Centre for Atmospheric Science dual-polarization X-band radar (NXPol) during the Convective Precipitation Experiment (COPE). In contrast to recent studies of ZDR columns focused on deep clouds that developed in high-instability environments, the COPE measurements show relatively frequent ZDR columns in shallower clouds, many only 4–5 km deep. The presence of ZDR columns is used to infer that an active warm rain process has contributed to precipitation evolution in convection deep enough for liquid and ice growth to take place. Clouds with ZDR columns were identified objectively in three COPE deployments, with both discrete convection and clouds embedded in larger convective complexes developing columns. Positive ZDR values typically extended to 1–1.25 km above 0°C in the columns, with ZDR ≥ 1 dB sometimes extending nearly 4 km above 0°C. Values above 3 dB typically occurred in the lowest 500 m above 0°C, with coincident airborne measurements confirming the presence of supercooled raindrops. Statistical analyses indicated that the convection that produced ZDR columns was consistently associated with the larger derived rainfall rates when compared with the overall convective population sampled by the NXPol during COPE.

Abstract. The ability of a fuzzy logic classifier to dynamically identify non-meteorological radar echoes is demonstrated using data from the National Centre for Atmospheric Science dual polarisation, Doppler, X-band mobile radar. Dynamic filtering of radar echoes is required due to the variable presence of spurious targets, which can include insects, ground clutter and background noise. The fuzzy logic classifier described here uses novel multi-vertex membership functions which allow a range of distributions to be incorporated into the final decision. These membership functions are derived using empirical observations, from a subset of the available radar data. The classifier incorporates a threshold of certainty (25 % of the total possible membership score) into the final fractional defuzzification to improve the reliability of the results. It is shown that the addition of linear texture fields, specifically the texture of the cross-correlation coefficient, differential phase shift and differential reflectivity, to the classifier along with standard dual polarisation radar moments enhances the ability of the fuzzy classifier to identify multiple features. Examples from the Convective Precipitation Experiment (COPE) show the ability of the filter to identify insects (18 August 2013) and ground clutter in the presence of precipitation (17 August 2013). Medium-duration rainfall accumulations across the whole of the COPE campaign show the benefit of applying the filter prior to making quantitative precipitation estimates. A second deployment at a second field site (Burn Airfield, 6 October 2014) shows the applicability of the method to multiple locations, with small echo features, including power lines and cooling towers, being successfully identified by the classifier without modification of the membership functions from the previous deployment. The fuzzy logic filter described can also be run in near real time, with a delay of less than 1 min, allowing its use on future field campaigns.

Abstract. Correct, timely and meaningful interpretation of polarimetric weather radar observations requires an accurate understanding of hydrometeors and their associated microphysical processes along with well-developed techniques that automatize their recognition in both the spatial and temporal dimensions of the data. This study presents a novel technique for identifying different types of hydrometeors from quasi-vertical profiles (QVPs). In this new technique, the hydrometeor types are identified as clusters belonging to a hierarchical structure. The number of different hydrometeor types in the data is not predefined, and the method obtains the optimal number of clusters through a recursive process. The optimal clustering is then used to label the original data. Initial results using observations from the National Centre for Atmospheric Science (NCAS) X-band dual-polarization Doppler weather radar (NXPol) show that the technique provides stable and consistent results. Comparison with available airborne in situ measurements also indicates the value of this novel method for providing a physical delineation of radar observations. Although this demonstration uses NXPol data, the technique is generally applicable to similar multivariate data from other radar observations.

Observations of the real-time state of the atmosphere are required in order to initialize numerical weather prediction (NWP) models. As NWP resolution improves, more observations are needed, to better capture regional variations in atmospheric conditions. In particular, surface observations are necessary to reflect conditions experienced on the surface. One proposed opportunity to increase the number of surface observations available for assimilation into NWP is to crowdsource the data from home weather stations. This study investigates the outdoor air temperature measurements made by Netatmo home weather stations, through validation against a calibrated laboratory chamber and by evaluating quality control schemes that are applied to a UK-wide network of Netatmo stations. In a series of controlled lab experiments, it was found that the Netatmo temperature sensor was accurate to 0.3 C. The response to fluctuations in temperature is lagged, with τ (the time taken for 63% of the change to be measured) calculated as 12.7 min for a nearinstantaneous decrease in temperature. Netatmo temperature observations were compared with Met Office MIDAS hourly weather observations. A warm bias in excess of 1 C was present in the Netatmo temperature observations, which was lessened by the three quality control schemes tested, but still in excess of 0.5 C. Hence, Netatmo temperature measurements have potential to be assimilated in NWP in the United Kingdom, but work is required to find a suitable agreed quality control scheme to filter out anomalous observations in the United Kingdom.

Abstract. The ability of a fuzzy logic classifier to dynamically identify non-meteorological radar echoes is demonstrated using data from the National Centre for Atmospheric Science dual polarisation, Doppler, X-band mobile radar. Dynamic filtering of radar echoes is required due to the variable presence of spurious targets, which can include insects, ground clutter and background noise. The fuzzy logic classifier described here uses novel multi-vertex membership functions which allow a range of distributions to be incorporated into the final decision. These membership functions are derived using empirical observations, from a subset of the available radar data. The classifier incorporates a threshold of certainty (25% of the total possible membership score) into the final fractional defuzzification to improve the reliability of the results. It is shown that the addition of linear texture fields, specifically the texture of the cross-correlation coefficient, differential phase shift and differential reflectivity, to the classifier along with standard dual polarisation radar moments enhances the ability of the fuzzy classifier to identify multiple features. Examples from the Convective Precipitation Experiment (COPE) show the ability of the filter to identify insects (18 August 2013) and ground clutter in the presence of precipitation (17 August 2013). Medium duration rainfall accumulations across the whole of the COPE campaign show the benefit of applying the filter prior to making quantitative precipitation estimates. A second deployment at a second field site (Burn Airfield, 6 October 2014) shows the applicability of the method to multiple locations, with small echo features, including power lines and cooling towers, being successfully identified by the classifier without modification of the membership functions from the previous deployment. The fuzzy logic filter described can also be run in near real time, with a delay of less than one minute, allowing its use on future field campaigns.

Observations of the precipitation rate/depth, drop-size distribution, drop-velocity distribution and precipitation type are compared from six in-situ precipitation sensor designs over 12 months to assess their performance and provide a benchmark for future design and deployment. The designs considered are: tipping-bucket (TBR), drop-counting (RAL), acoustic (JWD), optical (LPM), single-angle visiometer with capacitor (PWD21) and dual-angle visiometer (PWS100). Precipitation rates are compared for multiple time resolutions over the study period, while drop size and velocity distributions are compared with cases at stable precipitation rates. To examine precipitation type a new index and a logic algorithm to amalgamate consecutive precipitation type observations consistently is introduced and applied. Overall the choice of instrument for deployment depends on the usage. For fast response (less than 15 minutes), the PWD21 and TBR should not be used. As precipitation rate or the duration of a sample increases, the correlation of the TBR with the majority of other instruments increases. However, the PWD21 consistently underestimates precipitation. The RAL,PWS100 andJWDare within ± 15% for precipitation depth over 12 months. All instruments are inconsistent in their ability to observe drop size and velocity distributions for differing precipitation rates. There is low agreement between the instruments for precipitation type estimation. The PWD21 and PWS100 rarely report some precipitation types, but the LPM reports more broadly. Meteorological stations should use several instrument designs for redundancy and to more accurately capture precipitation characteristics.