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We present a deep-neural-network-based single-site stochastic precipitation generator (SPG), capable of producing realistic time series of daily and hourly precipitation. The neural network outputs a wet-day probability and precipitation distributions in the form of a mixture model. The SPG was tested in four different locations in New Zealand, and we found it accurately reproduced the precipitation depth, the autocorrelations seen in the original data, the observed dry-spell lengths, and the seasonality in precipitation. We present two versions of the hourly and daily SPGs: (i) a stationary version of the SPG that assumes that the statistics of the precipitation are time independent and (ii) a non-stationary version that captures the secular drift in precipitation statistics resulting from climate change. The latter was developed to be applicable to climate change impact studies, especially studies reliant on SPG projections of future precipitation. We highlight many of the pitfalls associated with the training of a non-stationary SPG on observations alone and offer an alternative method that replicates the secular drift in precipitation seen in a large-ensemble regional climate model. The SPG runs several orders of magnitude faster than a typical regional climate model and permits the generation of very large ensembles of realistic precipitation time series under many climate change scenarios. These ensembles will also contain many extreme events not seen in the historical record. [ABSTRACT FROM AUTHOR]

We present a deep neural network based single site stochastic precipitation generator (SPG), capable of producing realistic time series of daily and hourly precipitation. The neural network outputs a wet day probability and precipitation distributions in the form of a mixture model. The SPG was tested in four different locations in New Zealand, and we found it accurately reproduced the precipitation depth, the autocorrelations seen in the original data, the observed dry-spell lengths and the seasonality in precipitation. We present two versions of the hourly and daily SPGs: (i) a stationary version of the SPG that assumes that the statistics of the precipitation are time independent (ii) a non-stationary version that captures the secular drift in precipitation statistics resulting from climate change. The latter was developed to be applicable to climate change impact studies, especially, studies reliant on SPG projections of future precipitation. We highlight many of the pitfalls associated with the training of a non-stationary SPG on observations alone, and offer an alternative method that replicates the secular drift in precipitation seen in a large-ensemble regional climate model. The SPG runs several orders of magnitude faster than a typical regional climate model, and permits the generation of very large ensembles of realistic precipitation time series under many climate change scenarios, these ensembles will also contain many extreme events not seen in the historical record. [ABSTRACT FROM AUTHOR]

MAPM (Mapping Air Pollution eMissions) is a project whose goal is to develop a method to infer particulate matter (PM) emissions maps from in situ PM concentration measurements. In support of MAPM, a winter field campaign was conducted in New Zealand in 2019 (June to September) to obtain the measurements required to test and validate the MAPM methodology. Two different types of instruments measuring PM were deployed: ES-642 remote dust monitors (17 instruments) and Outdoor Dust Information Nodes (ODINs; 50 instruments). The measurement campaign was bracketed by two intercomparisons where all instruments were co-located, with a permanently installed Tapered Element Oscillating Membrane (TEOM) instrument, to determine any instrument biases. Changes in biases between the pre-and post-campaign intercomparisons were used to determine instrument drift over the campaign period. Once deployed, each ES-642 was co-located with an ODIN. In addition to the PM measurements, meteorological variables (temperature, pressure, wind speed and wind direction) were measured at three automatic weather station (AWS) sites established as part of the campaign, with additional data being sourced from 27 further AWSs operated by other agencies. Vertical profile measurements were made in two intensive radiosonde subcampaigns and were supplemented with measurements made with a mini micropulse lidar and ceilometer. Here we present the data collected during the campaign and discuss the correction of the measurements made by various PM instruments. We find that for while for the ODINs a correction based on environmental conditions is beneficial, this results in over-fitting and increased uncertainties when applied to the measurements obtained using the more sophisticated ES-642s. We also compare PM2.5 and PM10 measured by ODINs which, in some cases, allows us to identify PM from natural and anthropogenic sources. The PM data collected during the campaign are publicly available from https://doi.org/10.5281/zenodo.4023402 (Dale et al., 2020b), the data from other instruments are available from https://doi.org/10.5281/zenodo.4021685 (Dale et al., 2020a). [ABSTRACT FROM AUTHOR]

Background: Māori have known health disparities that may be addressed through increasing the cultural competency of New Zealand's medical workforce. There is a paucity of Māori health professionals choosing paediatrics or adult medicine as a career and the factors influencing their career decision are yet to be explored., Aims: First, to differentiate factors influencing the medical career choice of non-Māori paediatricians and physicians, Māori paediatricians and physicians and other Māori doctors. Second, to identify ways in which Māori doctors may be encouraged to choose paediatricians or adult medicine., Methods: A questionnaire was distributed by email to New Zealand physicians and paediatricians and to Māori doctors. Questions included demographic information, a matrix rating table and open-ended questions., Results: Altogether 199 people accessed the questionnaire. Response rates were 9% (n = 118) for non-Māori paediatricians and physicians, 70% (n = 19) for Māori paediatricians and physicians, and 31% (n = 62) for other Māori doctors. Māori paediatricians and physicians highlighted mentoring as having significant impact on career choice. Non-Māori paediatricians and physicians regarded interest as having the most influence on career choice (P < 0.01). Lifestyle factors influenced other Māori doctors (P < 0.001). All three groups regarded poor lifestyle as having the largest negative influence. No group regarded potential income as important., Conclusion: Mentoring provides an opportunity to attract Māori into paediatric and adult physician training. The use of existing mentoring programmes could facilitate in expanding Māori RACP workforce development. This extended Māori workforce would have benefits for the health of New Zealand as a whole., (© 2014 The Authors; Internal Medicine Journal © 2014 Royal Australasian College of Physicians.)