عنوان مقاله [English]
The lack of reliable and updated precipitation datasets is the most important limiting factor in studying many climatological and hydrological topics including climate change and temporal variability of precipitation in many data sparse areas around the globe. This is particularly valid for Iran that encompasses vast deserts and un-settled hyper-arid climate areas (central-eastern Iran) that hinders establishing an adequate network of rain-gauge stations required for climatological studies. Similarly, the high elevation areas of mountainous regions of western and northern Iran suffer from limited representative stations. Using the gridded or reanalysis precipitation datasets could be one of the possible solutions to overcome this obstacle; knowing that the representativeness of these datasets has been already proved for many different parts of the world. Amongst many available gridded precipitation datasets are the Global Precipitation Climatology Center (GPCC) and the Tropical Rainfall Measuring Mission (TRMM) that have been widely used in many researches; indicating their accurate estimation of precipitation values and intra-annual variation for the regions studied. The reanalysis precipitation dataset which is a product of the Numerical Weather Prediction (NWP) models is an alternative source of precipitation data that is widely used in the literature and many authors have pointed to the relatively accurate precipitation prediction of reanalysis for many parts of the word. The two widely used reanalysis datasets are NCEP/NCAR and different products of ECMWF, namely ERA-15, ERA-40 and ERA-Interim reanalysis. ERA-Interim which is used in the present study is produced at T255 spectral resolution (about 80 km) and covers the period from January 1979 to present, with product updates at approximately 1 month delay from real-time (Dee et al., 2011; Balsamo et al., 2015). The ERA-Interim atmospheric reanalysis is built upon a consistent assimilation of an extensive set of observations (typically tens of millions daily) distributed worldwide (from satellite remote sensing, in situ, radio sounding, profilers, etc.). To develop the reanalysis, the analysis step combines the observations with a prior estimate of the atmospheric state (first-guess fields) produced with a global forecast model in a statistically optimal manner (Balsamo et al., 2015).
The representativeness and performance of ERA-Interim in forecasting precipitation amount at 45 Iranian synoptic stations distributed across the country is herein examined. Spatial resolution of ERA-Interim dataset used in this study is 0.125 × 0.125 in latitude and longitude. For each station, the closest grid point of ERA-Interim to the station coordinates was chosen for a statistical comparison analysis. To evaluate the performance of the considered dataset when compared to the observed precipitation records at the considered locations we have used R squared, the Nash–Sutcliffe model efficiency coefficient (EF), RMSE, Bias, B slope of the regression and the standardized RMSE indicators. The performance of the dataset was also graphically represented through scatter plots of the established regression between ERA-Interim and observation at the selected stations. The results of the statistical indicators were represented through plotting the indicators over the map of Iran to ease displaying spatial tendency of the indicators and explaining the possible geographical role in controlling the spatial variation of the indicators. The results indicate that the ERA-Interim performs well in majority of the studied stations with strong correlation coefficient. However, it was found that the ERA-Interim underestimates precipitation in most of the stations located in the coastal areas of the Caspian Sea as well as in some stations along the Persian Gulf and the Oman Sea, suggesting that ERA-Interim is somewhat inefficient in adequately forecasting precipitation in the coastal areas; very likely due to not properly taking into account the complex topography of the region in its model parameterization or not being able to adequately differentiate between land and sea characteristics for the stations very close to the sea. It should be noted that the ERA-Interim is less efficient in accurately forecasting extreme precipitation in the Caspian Sea region. Nevertheless, we found very high agreement between observations and ERA-Interim in this region when some extreme precipitation events were excluded from the analysis. Contrarily, the results suggest an over-estimation for most of the stations located in northwestern and northeastern mountainous areas of the country; once again due to perhaps improper representation of topography of these regions in the model.