It is nearly impossible to accurately quantify rainfall variability across a stormwater or sewer catchment using discrete point rainfall measurements. The variability across the catchment can be significant depending on the catchment location and surrounding terrain. For many hydrological applications, such as sewer inflow and infiltration modelling, extrapolation of point rainfall measurements is standard practice and is one of largest unknowns in the model. Decisions about the techniques used for extrapolation, as well as the adequacy of the conclusions drawn from the modelling results, depend heavily on the magnitude and the nature of the uncertainty involved. In this paper we will outline our recent investigation using accurate short range radar in an attempt to quantify how standard point rainfall measurement and extrapolation techniques effect sewer model calibration and eventually options resulting from the model. In the highlighted case study we completed a detailed sewer model calibration using current industry best practice. As a second work stream we obtained radar data from the University of Auckland’s short range mobile radar unit for the entire monitoring period. We then tested the model calibration using the “true” rainfall distribution over each sewer-catchment as identified from the radar and commented on the variation in model calibration parameters and how the different rainfall distribution effects the perceived system performance and potential options analysis.

Reverse Kessler warm rain processes were implemented within the Weather Research and Forecasting Model (WRF) and coupled with a Newtonian relaxation, or nudging technique designed to improve quantitative precipitation forecasting (QPF) in New Zealand by making use of observed radar reflectivity and modest computing facilities. One of the reasons for developing such a scheme, rather than using 4D-Var for example, is that radar VAR scheme in general, and 4D-Var in particular, requires computational resources beyond the capability of most university groups and indeed some national forecasting centres of small countries like New Zealand. The new scheme adjusts the model water vapor mixing ratio profiles based on observed reflectivity at each time step within an assimilation time window. The whole scheme can be divided into following steps: (i) The radar reflectivity is firstly converted to rain water, and (ii) then the rain water is used to derive cloud water content according to the reverse Kessler scheme; (iii) The cloud water content associated water vapor mixing ratio is then calculated based on the saturation adjustment processes; (iv) Finally the adjusted water vapor is nudged into the model and the model background is updated. 13 rainfall cases which occurred in the summer of 2011/2012 in New Zealand were used to evaluate the new scheme, different forecast scores were calculated and showed that the new scheme was able to improve precipitation forecasts on average up to around 7 hours ahead depending on different verification thresholds.

The operation of a combined Nowcast/forecast rainfall presents a number of opportunities for the development of innovative hybrid techniques. As NWP is primarily an initial value problem, there is a reasonable expectation that by assimilating high-resolution observations of rainfall, cloudiness and wind speed the short-term skill of model predictions should be increased allowing a change over to NWP from Nowcasting at an earlier stage. Important work is underway in many groups who are attempting to assimilate radar observations of reflectivity and radial Doppler winds into convective scale models. For an island nation such as New Zealand the particular problem is that the radar reflectivity and radial velocity data are only available within a hundred kilometers or so of the coast meaning that the time ahead for relevant data to be assimilated into the air mass resident over the area of interest at the forecast time is quite limited. Possible solutions to this problem are the use of satellite data over the surrounding ocean regions to estimate rainfall and cloudiness, which in turn can be used in Nowcasting models the output of which can be used for direct assimilation into the NMP system. However, the optimum way to achieve this is not clear. We present some results and illustrate some problems for quantitative precipitation forecasts (QPF) obtained using WRF initialized with VAR using both directly derived radar and satellite rainfall and wind speed estimates and those obtained by Nowcasting.

The errors introduced into radar estimates of rainfall by making observations at spatial and temporal resolutions that are coarse compared with precipitation systems' characteristic length and time scales are explored in this study. High resolution (200 m, 50 s) X‐band radar data from 48 mid‐latitude precipitation events are downgraded progressively in spatial and temporal resolution so that estimates of this sampling error can be made by comparing 10 min rainfall accumulations of this data to accumulations calculated from the original high resolution data. The analysis shows these errors to be of significant magnitude. For 2 km and 5 min sampling, this error varies from 17 to 64% of the mean rainfall accumulation. A relationship is shown between the error introduced from the reduction in spatial resolution and the characteristic length scale of the precipitation system along with a metric of precipitation intensity. Copyright © 2011 Royal Meteorological Society

A portable field spectrometer was used to record the time-resolved ultraviolet/visible (UV/Vis) spectrum of the effluent stream at two different dairy processing plants (a Drier Plant and Cheese Factory). The spectra exhibited significant variability. As an alternative to the partial least squares regression methods usually used in the online UV/Vis field a non-negative matrix factorisation technique was employed to compress the spectral data. One of the extracted basis vectors had a physical shape associated with protein absorption. The weightings associated with the basis vector explained 80% of the variability in protein concentration as measured with traditional grab sampling techniques (increasing to 94% with a further three vectors), allowing the spectrometer to be retrospectively calibrated to continuously measure protein.

A new method for the simultaneous online measurement of sulfide and nitrate in wastewater is developed. A UV-VIS spectrometer was used. The sensor was calibrated by means of simultaneous online and offline measurements of sulfide and nitrate in batch tests carried out on a laboratory-scale sewer system. The developed calibration algorithm was successfully validated for both sulfide and nitrate measurement, with confidence limits of 2.7 mg S/L for total dissolved sulfide, and 7.5 mg N/L for nitrate. The online measurement of sulfide and nitrate enabled detailed evaluation of seven nitrate dosing strategies in the laboratory-scale sewer system, providing strong support to process optimisation. The dosage optimisation revealed that nitrate should be added at a location close to the point of sulfide control rather than at the beginning of a rising main, at a rate proportional to the expected hydraulic retention time (HRT) of the wastewater in the sewer section between the point of nitrate addition and the point where sulfide control is desired.

The University of Auckland Atmospheric Physics Group has operated mobile X-band radars for two decades. On 25 June 2005 the radar observed a small (F0) multiple tornado system passing over Ardmore in South Auckland. Reflectivity data from the tornadoes are of sufficiently high resolution to discern the wall structure and eye, track the tornadoes’ path and estimate the rotational velocity of the tornado and hence the core pressure drop. The additional value of radar at very high spatial and temporal resolution is illustrated.

The meat processing industry generates large volumes of relatively high load wastewater. In New Zealand and Australia this wastewater is often pre-treated on site and then discharged to environmental waters or municipal sewers. Owing to the limited number of water quality parameters which can be measured in real-time it is often difficult for industry to optimise treatment processes or public bodies to monitor for water-quality compliance. Abattoir wastewater is often observed to be red in colour, owing to the presence of haemoglobin. Measurement of visible light absorption spectra of wastewater grab samples has for some time provided information about blood concentration. However such grab sampling techniques are piecemeal and cannot provide instantaneous time resolved signals which are required for process control or comprehensive monitoring. In this work an in-situ UV/VIS spectrometer is used to continuously determine the concentration of haemoglobin in wastewater arriving for treatment at two different Wastewater Treatment Plants (WWTPs). The data is of high temporal resolution- data recorded at the distant WWTPs allows for identification process events, such as the end of shift wash downs.

Sulfides are particularly problematic in the sewage industry. Hydrogen sulfide causes corrosion of concrete infrastructure, is dangerous at high concentrations and is foul smelling at low concentrations. Despite the importance of sulfide monitoring there is no commercially available system to quantify sulfide in waste water. In this article we report on our use of an in situ spectrometer to quantify bisulfide in waste water and additional analysis with a pH probe to calculate total dissolved sulfide. Our results show it is possible to use existing commercially available and field proven sensors to measure sulfide to mg/l levels continuously with little operator intervention and no sample preparation.