Analysis of Iran temperature structure based on database output European Centre for Medium-Range Weather Forecasts (ECMWF) ERA Interim Version

  Air temperature is one of the most important climate measurements in the human environment, which directly affects the physical and biological processes of the ecosystem. Understanding this climate measure can be the basis for understanding many of the climatic processes, especially evapotranspiration (due to the climate of Iran). Reanalysis have been used in recent years in many studies, including studies on climate trends, climate modeling, and the assessment of renewable resources, and their high accuracy is confirmed.
The present study aimed at evaluating the accuracy of the open-baseline base temperature data of the European Centre for Medium-Range Weather Forecasts (ECMWF) of the ERA-Interim version with a 0.125 × 0.125 arc-spatial resolution in a survey with observational data from the weather stations and the National Bassoon database has been designed and implemented. In this regard, the temporal and temporal changes of the temperature of the country were also evaluated. In this study, data from 32 weather observation pods during the statistical period of 1979-2015 were used to validate the ESFAZRI national Database. ERA-Interim, also produced by ECMWF, uses 4D-variational analysis on a spectral grid with triangular truncation of 255 waves (corresponds to approximately 80 km) and a hybrid vertical coordinate system with 60 levels. The ECMWF global model is used for the forward integration in the 4D-variational analysis and the temporal length of the variational window is 12 h.
As stated for the validation of the temperature data of the ECMWF database of the ERA-Interim version, we verified the cells of this site with the data of the 32 well-selected Station in the period from 1979-2015. The nearest cells were selected to be sampled. To verify the two data sets, the R2 and RMSE indices were used. In order to evaluate the changes in Iran's monthly temperature, fractal dimension was calculated. The results of the validation between the European Centre for Medium-Range Weather Forecasts (ECMWF) and the ERA-Interim Emission and Interim National Projections for the period (1979-2015) showed that this base has a high performance, as is seen in most of the pioneering cases in this In the section, more than 98% of the coefficient of determination between the data of this base is observed with the data observed and recorded in the observer weather stations of the country. In the following summer, the fractal has reached its maximum value, reaching 1.63 in August for its grove over the year. Accordingly, the fractal dimension is increased and this increase reflects short-term variations, which means that the standard error also increases. In the cold period of the next year, fractals showed a decrease in value, reflecting short-term changes.
In the cold season from December to March, the average temperature varies from 7.2 to 7.3. The minimum air temperature varies from -3 to -6 degrees, and the maximum air temperature varies from 21.4 to 23.0 degrees Celsius. The dispersion of the December temperatures is more than in January and February. The data distribution was observed positively in December, 0.51, January 0.41, and 0.30 in December. In the cold months, the temperature distribution is more than positive, in fact, the values are less than the average of a higher frequency.
The average temperature in June was 28.2, 30 and 29 degrees Celsius, respectively. The range of changes in June, July and August was 22.3, 20.3 and 19.6 respectively, and the temperature diffraction in these months was 27.9, 20.19 and 18.4, respectively. The range of changes and dispersion in the months of July and August is higher than in September. The purpose of this study was to evaluate the mean air temperature based on the ERA Interim version of the European Centre for Medium-Range Weather Forecasts (ECMWF) data model. The results showed that the model was able to measure the temperature in the long run. The average of air temperature in all months of the year with the spatial component of the latitude is the highest correlation coefficient. The fractal dimension of the air temperature in the cold months is less than the warm months of the year. The highest fractal dimension occurs in the months of July and August coinciding with the warmest periods of the year, which indicates short-term changes due to the stability of the systems in the warm period of the year and long-term changes due to the variety of macro-scale systems in the cold period of the year. This statistic for Iran's temperature has shown that the climate and, more specifically, that the study has been tied to it, temperature is a complex and non-linear system, and has been composed of different measures and interactions.
According to the results, it can be stated that the southern regions of the country on the coast of Oman Sea and the Persian Gulf and the northern Persian Gulf in Khuzestan province require more attention regarding the days of cooling demand in the warm months of the year in order to adjust the air temperature in order to provide comfort in these areas. Should be. On the other hand, the pattern map for each month showed that the Northwest, high Zagros and Northeastern regions required more attention in terms of heating in the cold months of the year