Morteza Miri; Ghasem Azizi; Hossein Mohammadi; Mahdi Pourhashemi
Abstract
Extended Abstract
Introduction
The limited access to the atmospheric and terrestrial data such as rainfall, temperature, humidity and soil temperature is the most important problem in studying many climatological and hydrological in many parts of the world, particularly in developing countries, rural ...
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Extended Abstract
Introduction
The limited access to the atmospheric and terrestrial data such as rainfall, temperature, humidity and soil temperature is the most important problem in studying many climatological and hydrological in many parts of the world, particularly in developing countries, rural and mountainous areas. One of the solutions to overcome this obstacle is to use available gridded datasets that have proved their representativeness for many different parts of the world. Although the use of satellite data and gridded datasets is a reasonable alternative source for areas lacking station and data, since local effects can vary from region to region and can affect satellite and model performance, thus an dataset must be evaluated in a region before it is used as a decision-making tool in that region.
Materials and methods
The present study is aimed at the presentation of Global Land Data Assimilation System (GLDAS) and evaluates this model dataset against data measured by synoptic stations. The Global Land Data Assimilation System (GLDAS) has been developed jointly by scientists at the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC) and the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) in order to produce such fields. The goal of a land data assimilation system is to ingest satellite and ground-based observational data products, using advanced land surface modelling and data assimilation techniques. The uniqueness of GLDAS is that it is a global, high resolution, offline terrestrial modelling system incorporating ground and satellite observations. The temporal resolution for the GLDAS products is 3-hourly and Monthly with 0.25 and 1 degree spatial resolution its output is the result of four land surface models: the Community Land Model (CLM), NOAH, Mosaic, and the Variable Infiltration Capacity (VIC) model.The products are in Gridded Binary (GRIB) format and can be accessed through a number of interfaces.
The representativeness and performance of GLDAS in estimate temperature amount at 66 Iranian synoptic stations distributed across the country is herein examined. To evaluate the performance of the considered dataset when compared to the observed temperature 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 GLDAS and observation at the selected stations.
Results and discussion
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. According to the results of the evaluation, the GLDAS data performs well in all of the studied stations with strong correlation coefficient. However, the Special physiographic and climatic characteristics is one of the main reasons for this overestimation in the coastal areas of the Caspian Sea. very likely due to not properly taking into account the complex topography of the region in its model parameterization or not being able to remove the effect of sea atmosphere in the stations nearby the seas. However, since the cloud of the estimated data for this region are distributed along the regression line, it can be said that the observed over-estimation could be resolved through establishing a statistical relationship between the observed and modeled datasets; thus such a mismatch might not be considered as a drawback of the modeled dataset. Considering that this model output is produced through combination of the modeled, observed and remotely sensed data, it could be confidentially used for mountainous areas and deserts of Iran that suffer from lack of weather stations or substantial missing values. This data-set might be considered as a superior dataset to be used for many climatological and hydrological subjects in Iran and thus should be seen as a promising tool for extending hydrological and climatological research areas in the country.
Conclusion
Statistical comparisons indicate that the GLDAS data perform well in all of the studied stations with strong Accuracy. Due to the Global coverage of the model dataset, A large number of climate-hydrological variables, and the results of this research that indicate the Good accuracy of the GLDAS model in Iran, It is suggested that all variables in the model to be evaluated.
