Delineation of precipitation areas from MODIS visible and infrared imagery with artificial neural networks

An important phase in a nowcasting system is the diagnosis of the forecast variables. This work focuses on the diagnosis of precipitation. The Nimrod automatic nowcasting system at the Met Office (UK) has long used Meteosat visible and infrared data to supplement the data it receives from the UK weather radar network to produce rainfall analyses. With the advent of Meteosat Second Generation (MSG) attention has focused on how best to use the larger range of spectral information from MSG to improve the rainfall analyses. Earlier work at the Met Office had suggested artificial neural networks (ANNs) to be a useful tool for such applications. Pending the availability of data from MSG, ANNs were used to process data from appropriate visible and infrared channels on the MODIS instrument. Sixty daytime winter cases were collected, and Nimrod radar rainfall analyses provided 'ground truth' for both training and testing the ANNs. The optimal combination of MODIS channels was investigated and it was found that almost all the skill in rain/no rain discrimination was provided by the radiance values from six selected spectral channels. A notable result was that the 1.64 µm channel had no value as a discriminator when used alone, but produced a large increase in skill when used in conjunction with a visible channel. The ANN with MODIS data was found to outperform the corresponding Nimrod look-up table technique applied to Meteosat data. Application of the technique to SEVIRI data is proposed, as is extension to other seasons. Copyright © 2005 Royal Meteorological Societ

Using systems science to understand the determinants of inequities in healthy eating

Introduction: Systems thinking has emerged in recent years as a promising approach to understanding and acting on the prevention and amelioration of non-communicable disease. However, the evidence on inequities in non-communicable diseases and their risks factors, particularly diet, has not been examined from a systems perspective. We report on an approach to developing a system oriented policy actor perspective on the multiple causes of inequities in healthy eating. Methods: Collaborative conceptual modelling workshops were held in 2015 with an expert group of representatives from government, non-government health organisations and academia in Australia. The expert group built a systems model using a system dynamics theoretical perspective. The model developed from individual mind maps to pair blended maps, before being finalised as a causal loop diagram. Results: The work of the expert stakeholders generated a comprehensive causal loop diagram of the determinants of inequity in healthy eating (the HE2Diagram). This complex dynamic system has seven sub-systems: (1) food supply and environment; (2) transport; (3) housing and the built environment; (4) employment; (5) social protection; (6) health literacy; and (7) food preferences. Discussion: The HE2causal loop diagram illustrates the complexity of determinants of inequities in healthy eating. This approach, both the process of construction and the final visualisation, can provide the basis for planning the prevention and amelioration of inequities in healthy eating that engages with multiple levels of causes and existing policies and programs

Seasonal sensitivity of a VIS-NIR-IR rain-no rain classifier

Mid-latitude precipitation characteristics are influenced by the seasonal cycle: general circulation patterns, moisture distribution and cloud type occurrence vary throughout the year over a wide range of different structures. Since radiation in the visible-infrared part of the spectrum is sensitive to the cloud upper layers, the seasonal variability of the cloud structure is expected to affect the capabilities of satellite measurements to infer the precipitation at the ground. This work aims to assess and quantify the seasonal sensitivity of a statistical rain-no-rain classifier applied to data from the moderate resolution imaging spectroradiometer (MODIS) collected for summer and winter seasons over the UK region. In the first part, the satellite radiance measurement distributions for the two seasons were compared and discussed. Then, the comparison between satellite and ‘‘true’’ rain-no rain classification was carried out in term of statistical parameters (such as the Equitable Threat Score: ETS), showing their dependence on the dry to wet ratio of the statistical ensemble considered. Finally, by considering summer and winter datasets, the seasonal variability of MODIS rain-no rain classifier performance has been established and discussed. The sensitivity of the algorithm to the number and wavelengths of the channels used has been addressed, showing the high impact of the 1.6 mm channel if combined with one visible channel. The best performance was reached with six channels (0.85, 1.6, 3.9, 7.3, 8.5, and 12 mm), plus the solar zenith angle as additional input, for which the computed ETS is about 45% for summer and 37% for winter, keeping a fixed dry to wet ratio of 6. The use of an ‘‘annual’’ algorithm, trained with ensemble of summer and winter pixels, and applied on independent summer and winter ensembles, led to similar values for both summer and winter

Evaluation of a satellite multispectral VIS/IR daytime statistical rain-rate classifier and comparison with passive microwave rainfall estimates

A daytime surface rain-rate classifier, based on Artificial Neural Networks (ANN), is proposed for the Spinning Enhanced Visible and Infra Red Imager (SEVIRI), on board the Meteosat-8 geostationary satellite. It is developed over the British Isles and surrounding waters, where the Met Office radar network provided the \u201cground precipitation truth\u201d for training and validation. The algorithm classifies rain-rate in five classes at 15 minutes and 5 km of time and spatial resolution, and is applied on daytime hours in a summer and winter database. A further ANN application is restricted to hours between 12 and 14 UTC for which the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) on board the AQUA polar-orbiting satellite scans the U.K. area: ANN-classifier algorithms for the SEVIRI and AMSR-E data have been developed and the results compared. A reliable validation procedure is adopted in order to quantify the performance in view of the operational application of the daytime classifier and to investigate the relative skills of passive microwave and visible-infrared radiances in sensing precipitation if processed with equivalent algorithms. The key statistical parameters used are the Equitable Threat Score (ETS) and the BIAS for rain-no rain classes, and the Heidke Skill Score (HSS) for rain-rate classes. The SEVIRI daytime classifier shows, for mean seasonal conditions, the best performance in summer, with ETS=47% and HSS=22%, while in winter ETS=36% and HSS=17% were found. The comparison between AMSR-E and SEVIRI noon classifiers reveals a similar overall skill: in detecting rain areas SEVIRI is slightly better than AMSR-E, while the opposite is true for rain-rate classification