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

Air quality in urban areas is often dominated by vehicle emissions but pollutants such as volcanic ash, dust from bush fires and particles from dust storms also contribute. This paper presents a semi-empirical methodology for quantifying the relative contribution of these sources. The technique is based on the underlying premise that vehicles follow a diurnal pattern of emissions (whereas the other sources do not) but that both are modulated by the surface wind flows. The methodology is evaluated using rainfall washout as a removal mechanism (rather than an additional source). Whereas gaseous pollutants are relatively insoluble in water, particulate matter is readily removed. The extent of the washout is observed as the difference between the model predicted and observed concentrations. The same methodology can be applied to quantify the relative contribution of pollution sources such as ash.

Twenty‐seven radar cells from the Tropical Atlantic observed during GATE were followed and measurements of their fluxes and areas for initial time increments T0 were fitted to various extrapolation schemes. The extrapolation procedure that gave the smallest error inforecasting the changes influx and area, was found to be the linear one and the optimum increment T0 was about 30 min. However, even though these techniques have the advantage of establishing a trend in the behaviour of the flux and area with time, a comparison of the forecast errors from the linear extrapolation scheme with those from the “status quo” (persistence) assumption shows little if any improvement. A technique including both cell motion and internal changes influx and area of the rain cells was developed to evaluate the accuracy of rain accumulation forecasts. It was found that the errors generated by the “status quo” assumption were of the order of 77% for a 2‐h forecast with little improvement by allowing for the extrapolation of area and flux.

The Atmospheric Physics Group runs a number of high resolution X-band mobile rain radars. The radars are unusual in that they operate at very high spatial and temporal resolution but short range (100m/20sec/20km) as compared with the C-band radars of the New Zealand Meteorological Service (2km/7min/240km). Portability was a key design criterion for the radars, which can either be towed by a personal four wheel drive vehicle or carted by a container truck. Past deployments include the slopes of an erupting volcano, the path of a tropical storm and overwintering in a mountain range. It is well known that sampling and representativeness problems associated with sparse gauge networks and C-band radars can result in high uncertainty in estimates of aerial rainfall. Some of this error is associated with poor sampling of the spatial and temporal scales which are important to precipitation processes. In the case of long range radar, the beam height increase with range also introduces uncertainty when trying to infer precipitation at the ground, even after reflectivity profile correction methods are applied. This paper describes a recently completed field campaign in a hydro power catchment in the North Island of New Zealand. The radar was deployed in a pasture on a farm overlooking the catchment which is about 15km x 10km in size. The catchment is about 150km from the nearest national C-band radar. A number of rain gauges, including high resolution drop counters, were deployed nearby. X-band and comparative C-band radar observations of particular events including orographically initiated convection, frontal systems and widespread rain types are presented. The convective events are characterised by short length scales and rapid evolution, but even the widespread rain has embedded structure. The observations indicate that the evolution time and spatial scales associated with many of the hydrometeors observed in this work precludes aerial estimates being made with sparse gauge networks. Due to the relatively long range and lower spatial and temporal resolution the C-band images contained less information than X-band scans of the same hydrometeors. On the other hand, per event statistics indicate that the majority of variance in rain gauge measurements can be explained from the co-located X-band radar pixel. Quantitative retrieval of accumulation was possible out to about 15km range after applying range and bias correction.

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 discrete angle radiative transfer systems discussed in part 1 readily lend themselves to approximation schemes in which simple scaling systems with known radiative transfer properties can be doubled in size yielding analytic expressions relating the transfer coefficients corresponding to the initial and doubled scale. This "real space renormalization" method can be viewed as a generalization of conventional invariant imbedding techniques to scaling systems. Analytic nonlinear doubling mappings are obtained for homogeneous square, cubic and triangular systems, as well as for a simple fractal system with both open and cyclic horizontal boundary conditions. The doubling mappings have both thick and thin cloud fixed points; to which the transmission and albedoes are respectively algebraically attracted and repelled, with universal (phase function independent) exponents we estimate analytically. The method is approximate since it systematically neglects small-scale intensity gradients; however, the results are qualitatively correct, and it therefore establishes the connection between the scaling of the cloud optical density field and the scaling of the corresponding transfer coefficients. We also discuss the limitations of the method; in part 3 we compare it with a numerical approach.

The University of Auckland global Mars mesoscale meteorological model (GM4) is a numerical model of the martian atmosphere that has been developed through the conversion of the Penn State University/National Center for Atmospheric Research fifth generation mesoscale model (MM5). The model is initialized in this paper through the implementation of a ‘base-state’ atmosphere. Continual boundary condition input from a GCM is unnecessary as the global domains of the model are self consistent and form a continuous domain around the entire planet. A description of the model and its basic underlying physical principles as applicable to the atmosphere of Mars is outlined. Comparison between ASI/Met data collected from Mars Pathfinder during its 1997 mission and simulated conditions using GM4 is given. Diurnal temperature variation as predicted by the model shows very good correspondence with the measured surface data, to within 5 K for the majority of the diurnal cycle. Mars Viking I surface meteorological data is compared to the GM4 model, yielding similar results. To assess the vertical structure of the atmosphere, simulations have also been compared with Mars Global Surveyor Radio Science temperature–pressure profiles. As a further test for the model, various seasonal comparisons of surface and vertical atmospheric structure are performed with the European Space Agency AOPP/LMD Mars Climate Database. Agreement between the two models is reasonable, though polar regions are not very well represented by the GM4 model at present. As an experimental case study, mountain flow over Olympus Mons is simulated using this new mesoscale modeling system, showing results in good agreement with similar model simulations and observational data.

New Zealand has had an active programme in the exploration of microphysical processes involved in rainfall. This may be due in part to the ready availability of experimental targets in at least parts of the North Island and certainly the West Coast of the South Island. Initially, drop size measurements taken in Auckland were directed towards understanding the microphysical processes, including electrical effects, involved in the development of rainfall and lately, in support of weather radar work. The purpose of the paper is to place the New Zealand work in an international context.

The Sixth International Symposium on Hydrological Applications of Weather Radar was hosted by the Australian Bureau of Meteorology in Melbourne during February 2004. The unique aspect of the conferences is that they are traditionally attended by hydrologists as well as radar meteorologists. Dialogues between the two groups have proven to be relatively rare and often at rather crossed purposes, so this conference series provides a useful forum for discussion between these two groups. Discussions as to what accuracy hydrologists want in their rain-field data as well as questions as to the accuracy that can be provided have generally not been particularly helpful. What is clear is that weather radar provides the best rainfall estimates for convective rainfall patterns and will continue to do so in the foreseeable future. Moreover, the radar data are capable of providing short-term forecasts by image extrapolation, which is emulated with difficulty by gauge networks. Thus, we must try our best with existing and new networks of weather radars. Certain trends are evident when looking back at the proceedings for the previous Symposia; notably, the impact of networks of weather radars on the use of radar data in hydrological applications, and the need for a sustained focus on characterising and mitigating the errors that are inherent in radar estimates of rainfall. This is not surprising since inadequate radar coverage of areas of interest to hydrologists and rainfall estimates with an unknown but suspect quality are major impediments to using radar data operationally. The major theme in the call for papers for the Sixth Symposium was 'Success Stories in Radar Hydrology' and it is clear that radar networks exist in many countries but a lament regarding the lack of progress in radar hydrology was a recurrent theme in the presentations. Radar hydrology remains a work in progress; much has been done and yet much remains to be done. There has been considerable progress in developing networks of radars that are dense enough to provide useful estimates of rainfall over large areas, the major sources of errors have been identified, and the number of operational applications using radar rainfall estimates is increasing. Realistic schemes for combining the radar data with other meteorological data and using the combined result for hydrological predictions such as those for floods are surprisingly rare. Although there appears to be funding to install and operate the radar networks, similar funding for the development of integrated operational radar hydrological systems is not generally available. Much research remains to be done before a map of probable error can be estimated and presented alongside the rainfall map in real time, and yet this has to be an achievable goal. A smaller workshop on the measurement, modelling and consequences of the fine-scale structure of rainfall patterns was conducted in Auckland, New Zealand immediately after the main conference. This meeting brought together a number of groups working with high-resolution radars and high spatial and temporal rain gauge networks. A problem highlighted was the very large sampling errors encountered when radar data are compared with sparse gauge data due to the large discrepancy in sampling volumes. The calibration of radars with rain gauges is thus not a trivial exercise particularly for rain fields with high spatial inhomogeneity. A variety of modelling schemes that have the capability of addressing this statistical problem was presented. The editors would like to thank the members of the International Programme Committee for their input into the Symposium and the local organising committee for their assistance.

There are many cases where observed orographic enhancement has been plausibly attributed to the seeder feeder mechanism. To date, however, there have been few examples of radar data that clearly illustrate the concept of process, in the manner that the data presented here do. In this case study, vertical pointing radar data collected on the windward side of New Zealand's Southern Alps show two separated precipitating systems, with shallow rainfall enhanced by snowfall from aloft. Simple analysis of the synoptic conditions and rainfall data showed that this interaction did result in orographic enhancement. Further evidence of a seeder feeder event is given by simultaneous scanning radar data, which show a marked increase in raining area at the onset of the snowfall.

The deployment of weather radar, notably in mountainous terrain with many microclimates, requires the use of several or even many drop size spectrometers to provide confidence in the quantitative relation between radar reflectivity and rainfall. While there are several different commercial disdrometers available they are all expensive, large, or fragile, which militates against multiple deployment in the field. The design brief was for a reasonably accurate and sensitive, low-cost and rugged disdrometer to support field work. A design based on piezoceramic disks normally used in hydrophones is described. Calibration and typical field results are presented.

A new method of constraining divergent attenuation corrections for weather radar systems is presented. This was motivated by the need for reliable attenuation corrections when making quantitative precipitation estimates using a small, mobile X-band radar at short range. The approach is suitable for systems requiring attenuation correction in real-time and requires no auxiliary data. An outline of the literature on attenuation and its correction for single-polarisation weather radar is presented. The traditional form of correction is known to be problematic due to the divergence, which may occur in its estimation. The form of constraint presented is based on the representation of attenuation correction originally derived for space-borne radar configurations. This method determines when divergent estimates will occur and allows a more realistic application of the constrained correction. Examples of attenuation correction applied to X-band radar observations in comparison with ground clutter returns are presented, showing good agreement and hence good absolute calibration. Selected profiles are then used to determine the influence of measurement errors on attenuation estimation in the context of this representation. The paper concludes with a discussion of the practical limitations and considerations in applying an attenuation correction to quantitative weather radar rainfall estimates.