Search

In regional studies, reanalysis datasets can extend precipitation time series with insufficient observations. In the present study, the ERA5 precipitation dataset was compared to observational datasets from meteorological stations in nine different precipitation zones of Iran (0.125° × 0.125° grid box) for the period 2000–2018, and measurement criteria and skill detection criteria were applied to analyze the datasets. The results of the daily analysis revealed that the correlation between ERA5 and observed precipitation were larger than 0.5 at 90% of stations. Also, The daily standard relative bias indicated that precipitation was overestimated in zone 6. As detection criteria, the frequency bias index (FBI) and proportion correct (PC) showed that the ERA5 data could capture daily precipitation events. Correlation confidence comparisons between the ERA5 and observational time series at daily, monthly, and seasonal scales revealed that the correlation confidence was higher at monthly and seasonal scales. The standard relative bias results at monthly and seasonal scales followed the daily relative bias results, and most of the ERA5 underestimations during the summer belonged to zone 1 in the coastal area of the Caspian Sea with convective precipitation. In addition, some complex mountainous regions were associated with overestimated precipitation, especially in northwest Iran (zone 6) in different time scales.

The paper studies time series of characteristic (minimum, mean, and maximum) daily, monthly, and yearly air temperatures measured at the Zagreb Grič Observatory in the period from 1 Jan. 1881 to 31 Dec. 2017. The following five air temperatures indices (ATI) are analysed: (1) absolute minimum yearly, monthly, and daily; (2) mean yearly, monthly, and daily minimum; (3) average mean yearly, monthly, and daily; (4) mean yearly, monthly, and daily maximum; (5) absolute maximum yearly, monthly, and daily. Methods of Rescaled Adjusted Partial Sums (RAPS), regression and correlation analyses, F-tests, and t-tests are used in order to describe changes in air temperature regimes over 137 years. Using the RAPS method the five analysed yearly ATI time series durations of 137 years were divided into two sub-periods. The analyses made in this paper showed that warming of minimum air temperatures started in 1970, mean air temperatures in 1988, and maximum air temperatures in 1998. Results of t-tests show an extreme statistically significant jump in the average air-temperature values in the second (recent time) sub-periods. Results of the t-tests of monthly temperatures show statistically significant differences between practically all five pairs (except in two cases) of analysed monthly ATI subseries for the period from January to August. From September to December the differences for most of pairs (except in six cases) of the analysed monthly ATI subseries are not statistically significant. It can be concluded that the urban heat island influenced the increase in recent temperatures more strongly than global warming. It seems that urbanisation firstly and chiefly influenced the minimum temperatures, as well as that Zagreb’s urbanisation had a bigger impact on minimum temperatures than on maximums. Increasing trend in time series of maximum temperatures started 20 years later.

The main objective of this paper is to present a model for generating synthetic rainfall totals on various timescales to be applicable for a variety of uses. Many large-scale ecological and water resource models require daily, monthly and yearly rainfall data as input to the model. As historical data provides only one realisation, synthetic generated rainfall totals are needed to assess the impact of rainfall variability on water resources systems (Srikanthan, In: MODSIM2005, Melbourne, Dec. 2005, pp. 1915–1921, 2005). Thus, our preferred model should simulate rainfall for yearly, monthly and daily periods. We believe that, for water supply issues, no higher resolution is needed, although higher resolution would be useful in models designed to measure the risk of local flooding. The critical factors are daily, monthly and yearly totals and daily, monthly and yearly variation. A model for generating yearly totals will be described using traditional time series methods. This model, along with a similarly constructed daily generation model by Piantadosi et al. (A New Model for Correlated Daily Rainfall, 2008), will be cascaded to start with a synthetic yearly total, then generate a synthetic sequence of monthly totals (through selection from a large number of realisations) that match the yearly total, and subsequently perform a similar operation for sequences of daily totals to match the required monthly totals. We present a new model for the generation of synthetic monthly rainfall data, which we demonstrate for Parafield in Adelaide, South Australia. The rainfall for each month of the year is modelled as a non-negative random variable from a mixed distribution with either a zero outcome or a strictly positive outcome. We use maximum likelihood to find parameters for both the probability of a zero outcome and the gamma distribution that best matches the observed probability density for the strictly positive outcomes. We describe a new model that generates correlated monthly rainfall totals using a diagonal band copula with a single parameter to generate lag-1 correlated random numbers. Our model preserves the marginal monthly distributions and, hence, also preserves the monthly and yearly means. We show that, for Parafield, the correlation between rainfall totals for successive months is not significant, and so, it is reasonable to assume independence. This is, however, not true for daily rainfall. We describe a new model that generates correlated daily rainfall totals using a diagonal band copula with a single parameter to generate lag-1 correlated random numbers.

The need for long time series of gridded meteorological data with a fine spatial and temporal resolution has increased in recent years. The requirements for this type of gridded meteorological data fields arise from many different areas of the society, in connection to atmospheric environment studies of air quality and deposition and trends in these parameters, regional climate change, wind energy, hydrological studies etc. The aim of the present project is to investigate the possibility of producing historical, high quality and time consistent, meso-scale re analyses for the whole of Europe regarding precipitation, 2 m temperature and wind for at least 25 years back in time.The MESAN analysis system (Häggmark et al., 2000) at SMHI was chosen as a basis for the reanalysis and the system was adjusted to cover the whole of Europe. In order to find the most appropriate first guess fields to be used in the MESAN system, a pilot study was performed. ERA- 40 data from ECMWF was selected as best possible first guess fields for the re analysis. The performed re-analysis, which is denoted ERAMESAN, includes gridded data covering all Europe with a time resolution of 6 h and a spatial resolution of 0.1º (11 km) in a rotated latitude longitude coordinate system for the time-period 1980-2004. All analyses are archived in GRIB-format and stored on disc at SMHI. The dataset is also available within the EUMETNET optional programme Showcase EUROGRID.A partial validation for the years 1998-2000, using a cross validation procedure with independent observations (5.5% of the total amount of stations), shows an improvement in ERAMESAN compared to the ERA-40 data for all studied parameters with regard to root mean square deviation, mean absolute deviation and mean bias deviation for all seasons. The deviations are roughly of the order of 15% smaller compared to what is obtained from ERA-40. The frequency distribution of large precipitation amounts per day and high wind speeds are substantially better described in ERAMESAN compared to ERA-40. However, the tendency to underestimate the frequency of very large precipitation amounts or high wind speeds, compared to observations, can be seen also for ERAMESAN. It is important to be aware of this limitation when using ERAMESAN data for practical applications concerning evaluation of risks for extreme wind speeds or very large precipitation amounts or in e.g. wind energy studies.