SL-AV atmospheric model version using σ-<Emphasis Type="Italic">p</Emphasis> hybrid vertical coordinates

The SL-AV atmospheric model version using hybrid vertical coordinates combies the advantages of sigma and isobaric coordinates. The formulation and discretization of model equations maintain the equivalency of the new model version to the basic sigma version in the special case, when hybrid coordinates coincide with sigma coordinates. The SL-AV model version with hybrid vertical coordinate is verified with medium-range weather forecasts. The decrease in the errors of predicted geopotential height and wind as compared to the sigma model version is demonstrated. The use of hybrid coordinates also leads to a certain increase in forecast skill scores for some meteorological parameters characterizing aviation significant weather.     

Statistics of the <Emphasis Type="Italic">Z</Emphasis>–<Emphasis Type="Italic">R</Emphasis> Relationship for Strong Convective Weather over the Yangtze–Huaihe River Basin and Its Application to Radar Reflectivity Data Assimilation for a Heavy Rain Event

The relationship between the radar reflectivity factor (Z) and the rainfall rate (R) is recalculated based on radar observations from 10 Doppler radars and hourly rainfall measurements at 6529 automatic weather stations over the Yangtze–Huaihe River basin. The data were collected by the National 973 Project from June to July 2013 for severe convective weather events. The Z–R relationship is combined with an empirical q_r–R relationship to obtain a new Z–q_r relationship, which is then used to correct the observational operator for radar reflectivity in the three-dimensional variational (3DVar) data assimilation system of the Weather Research and Forecasting (WRF) model to improve the analysis and prediction of severe convective weather over the Yangtze–Huaihe River basin. The performance of the corrected reflectivity operator used in the WRF 3DVar data assimilation system is tested with a heavy rain event that occurred over Jiangsu and Anhui provinces and the surrounding regions on 23 June 2013. It is noted that the observations for this event are not included in the calculation of the Z–R relationship. Three experiments are conducted with the WRF model and its 3DVar system, including a control run without the assimilation of reflectivity data and two assimilation experiments with the original and corrected reflectivity operators. The experimental results show that the assimilation of radar reflectivity data has a positive impact on the rainfall forecast within a few hours with either the original or corrected reflectivity operators, but the corrected reflectivity operator achieves a better performance on the rainfall forecast than the original operator. The corrected reflectivity operator extends the effective time of radar data assimilation for the prediction of strong reflectivity. The physical variables analyzed with the corrected reflectivity operator present more reasonable mesoscale structures than those obtained with the original reflectivity operator. This suggests that the new statistical Z–R relationship is more suitable for predicting severe convective weather over the Yangtze–Huaihe River basin than the Z–R relationships currently in use.     

<Emphasis Type="Italic">Ua ‘afa le Aso</Emphasis> Stormy weather today: traditional ecological knowledge of weather and climate. The Samoa experience

This paper examines traditional ecological knowledge of weather and climate in Samoa, a Polynesian community in the South Pacific. The research found Samoans have their own unique seasonal calendar. The Samoan seasonal calendar is predominantly based on the observations of local environmental changes, which are in turn influenced by weather and climate. Monitoring changes in plants and animal behaviour, for example, are key indicators used by the Samoans to forecast changes in weather and climate. In addition, their communal and family social activities like hunting, fishing and feasting are driven by the seasonal calendar. The Samoans knowledge of cloud formation, conditions conducive to the formation and onset of severe weather systems and seasonal changes in climate, helped them anticipate, plan and adapt to extreme weather and climate events. The ability and knowledge of the Samoans to forecast the onset of extreme weather and climate events, relying predominantly on local environmental changes are vital tools that should be incorporated in the formulation of human induced climate change adaptation strategies.     

Statistical modelling of the main features of the <Emphasis Type="Italic">Artemisia</Emphasis> pollen season in Wrocław,Poland, during the 2002–2011 time period

The aim of this article is to present statistical forecasting models concerning the dynamics of Artemisia pollen seasons in Wroc?aw, including the start and end, the date of maximum pollen concentration and seasonal pollen index (SPI). For statistical evaluation, use was made of aerobiological and meteorological data from the last 10 years (2002–2011). Based on this data, agroclimatic indicators, i.e. crop heat units (CHUs), were determined for various averaging periods. The beginning of the Artemisia pollen season in the studied time period, on average, took place on 23 June. Its length usually varied between 26 and 45 days, and maximum daily concentrations occurred between 31 July and 18 August. It was found that the beginning of the pollen season depends, above all, on the values of CHUs and photothermal unit (PTU) (p?<?0.05) in the period from March to June, for various thermal thresholds. The date of maximum daily concentration correlates with sunshine duration, PTU and air temperature for June and July (p?<?0.05). On the other hand, SPI is connected with thermal variables, i.e. average, maximum and minimum air temperatures and CHUs and heliothermal unit (HTU) for July (p?<?0.05) and the beginning of spring. Based on the correlation analysis and the chosen variables, regression models for the beginning date of Artemisia pollen season and SPI were prepared, which were then verified by using leave-one-out cross-validation. A better fit between modelled and actual values was found for the analysis concerning the season start date than for the SPI.