Comparative assessment of evapotranspiration derived from NCEP and ECMWF global datasets through Weather Research and Forecasting model

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1 ATMOSPHERIC SCIENCE LETTERS Atmos. Sci. Let. 14: (2013) Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: /asl2.427 Comparative assessment of evapotranspiration derived from NCEP and ECMWF global datasets through Weather Research and Forecasting model Prashant K. Srivastava,*Dawei Han, Miguel A. Rico Ramirez and Tanvir Islam Water and Environment Management Research Centre, Department of Civil Engineering, University of Bristol, UK *Correspondence to: P. K. Srivastava, Water and Environment Management Research Centre, Department of Civil Engineering, University of Bristol, Bristol, BS8 1TR, UK. Received: 14 December 2012 Revised: 30 January 2013 Accepted: 30 January 2013 Abstract In many hydro-meteorological applications, it is not always possible to get access to in situ weather measurements, especially for the ungauged catchments. This study explores the performances of downscaled weather data for Reference Evapotranspiration (ET o ) retrieval using the global European Centre for Medium Range Weather Forecasts (ECMWF) ERA interim and National Centers for Environmental Prediction (NCEP) reanalysis data, simulated through Weather Research and Forecasting (WRF) mesoscale model. The range of the Nash-Sutcliffe efficiency calculated for the ECMWF pooled datasets derived ET o varies from 0.31 to 0.87, while for NCEP it is found to be 0.11 to Bias and Root Mean Square Error (RMSE) are also indicating a very high discrepancy in the NCEP ET o (Bias = 0.05; RMSE = 0.11) as compared to ECMWF (Bias = 0.00; RMSE = 0.06). The overall findings reveal that ECMWF downscaled products have a much better performance than the NCEP s counterparts. Copyright 2013 Royal Meteorological Society Keywords: evapotranspiration; Weather Research and Forecasting model; NCEP; ECMWF; meteorological variables 1. Introduction Reference Evapotranspiration (ET o ) is an important variable for hydro-meteorological applications (Liguori and Rico-Ramirez, 2013). ET o has significant effect on catchment water balance and hence water yield and groundwater recharge (Al-Shrafany et al., 2012) and its reliable estimates from cropped surfaces are required for efficient irrigation management and scheduling (Lorite et al., 2012). Hence, monitoring ET o at local, regional or global scales is important for assessing climate and human-induced effects on natural and agricultural ecosystems (Kustas and Norman, 1996; Thakur et al., 2012). However, to reduce the uncertainty in the ET o estimates, appropriate meteorological data selection is important if derived from mesoscale models (Evans et al., 2011). There are now choices of global data available for ET o estimation with reliable mesoscale model like the Weather Research and Forecasting (WRF) model for meteorological applications (Niyogi et al., 2009; Bromwich et al., 2009). Many methods for estimating ET o have been developed, and accurate estimates of ET o are becoming available through the use of ground-based observations using the Penman Monteith equation (Novák, 2012). But these ground-based observations can cover only a smaller area. However, larger areas require a large number of observation sites because of the heterogeneity of landscapes and very high variations in the energy transfer processes (Detto et al., 2006). Nevertheless, the above-mentioned approaches are very expensive and labour intensive, so other approaches are required such as mesoscale models to estimate ET o at large scales. Recently, efforts have been made to determine the spatial and temporal variability of ET o through mesoscale model like the MM5 (Ishak et al., 2010; Niyogi et al., 2009) but there is a lack of appropriate studies available with the WRF model, especially for temperate maritime climate. There are very rare studies available which demonstrated the accuracy of data chosen for the ET o estimation from mesoscale model such as WRF using the global European Centre for Medium Range Weather Forecasts (ECMWF) ERA interim and NCEP (National Centers for Environmental Prediction) reanalysis data to determine the spatial and temporal distribution of ET o (Buizza et al., 2005). Therefore hydro-meteorologists would like to know how well the downscaled global data products are as compared to ground-based measurements and whether it is possible to use the downscaled data for ungauged catchments. Even with gauged catchments, most of the stations have only rain and flow gauges installed. Measurements of other weather hydro-meteorological variables such as solar radiation, wind speed, air temperature and dew point are usually missing and thus complicate the problems. Hence, the foremost objective of this article is to compare the ET o products estimated from the downscaled ECMWF Copyright 2013 Royal Meteorological Society

2 NCEP and ECMWF comparison 119 and NCEP global datasets using the WRF model, centred over the Brue catchment in south west of the England. The validations of the products are made by using the ground-based measurements retrieved from meteorological weather station located in the Brue catchment. 2. Materials and methodology 2.1. Study area and datasets The Brue catchment (135.5 km 2 ) is chosen as the study area which is located in the south-west of England, N and 2.47 W. The major land use/land cover is pasture land on clay soil with some patches of woodland in the higher eastern catchment. The land use/land cover of Brue is illustrated in Figure 1 with the Digital Elevation Model and land use. The meteorological datasets are provided by the British Atmospheric Data Centre (BADC), UK that includes wind speed, net radiation, surface temperature and dew point. Despite the increasing computing power from desktop PCs, downscaling by the WRF at a high spatial and temporal resolution is still quite time consuming. As a result, only 4 months (January 2011, April 2011, July 2011 and October 2011) of data have been analysed in this study corresponding to four seasons in UK that is winter, spring, summer and autumn. The data provided by BADC are used for evaluating the hourly downscaled meteorological data from the WRF model Version 3.1. The global ECMWF ERA interim and NCEP reanalysis data can be downloaded from their respective websites (ECMWF- and NCEP- The ERA interim dataset is updated in monthly batches with 3 months delay and has a resolution of T255 (triangular truncation at 255), N128 (128 latitude circles, pole to equator), L60 (model levels), 37 pressure levels and 15/16 isentropic levels ( the NCEP FNL (Final) Operational Global Analysis data are on grids prepared Figure 1. Geographical location of the study area with digital elevation model, land use and observation stations.

3 120 P. K. Srivastava et al. operationally every 6 h. This product is from the Global Data Assimilation System, which continuously collects observational data. The archive time series is continuously extended to a nearcurrent date but not maintained in real-time ( The main purposes of these reanalysis data are to deliver compatible, high-resolution and high quality historical global atmospheric datasets for their use in weather research communities Weather Research Forecasting model The mesoscale model used in this study is the WRF Model with Advanced Research WRF dynamic core version 3.1 (Powers, 2007; Schwartz et al., 2009). WRF is a next-generation, non-hydrostatic, with terrain following eta-coordinate mesoscale modelling system designed to serve both operational forecasting and atmospheric research needs (Skamarock and Klemp, 2008). We choose WRF in this study because it is being developed and studied by a broad community of government and university researchers and results are quite efficient (Skamarock et al., 2005). The WRF model is centred over the Brue catchment with three nested domains (D1, D2 and D3) of horizontal grid spacing of 81, 27 and 9 km, in which the innermost domain (D3) is the area of interest. These three domains consisted of 18 18, and horizontal grids points. A two-way nesting scheme is used allowing information from the child domain to be fed back to the parent domain. Imposed boundary conditions are updated every 6 h when using the ECMWF or NCEP Final Analysis (1 1 FNL) dataset. The WRF model is used to downscale the ECMWF and NCEP data to predict wind speed, solar radiation, surface temperature and dew point temperature. The main physical options used in the WRF setup were the Dudhia shortwave radiation (Dudhia, 1989) and Rapid Radiative Transfer Model long wave radiation (Mlawer et al., 1997) with Lin microphysical parameterization; the Betts Miller Janjic (BMJ) Cumulus parameterization schemes; the Yonsei University planetary boundary layer scheme (Hu et al., 2010). The BMJ cumulus parameterization scheme is used because it considers sophisticated cloud mixing scheme in order to determine entrainment/detrainment which is found to be more applicable to non-tropical convection (Gilliland and Rowe, 2007). The thirdorder Runge Kutta is used for the time integration while for spatial differencing scheme the sixth-order centred differencing scheme is used. The Arakawa C-grid is used for the horizontal grid distribution. The Thermal diffusion scheme is used for the surface layer parameterization. The top and bottom boundary condition chosen for the study are Gravity wave absorbing (diffusion or Rayleigh damping) and physical or free-slip respectively. The Lambert conformal conic projection is used as the model horizontal coordinates. The vertical coordinate η is defined as: η = (p r p t ) (1) (p rs p t ) where, P r is pressure at the model surface being calculated; p rs is the pressure at the surface and P t is the pressure at the top of the model. In the vertical 28 terrain following eta levels (eta levels = 1.000, 0.990, 0.978, 0.964, 0.946, 0.922, 0.894, 0.860, 0.817, 0.766, 0.707, 0.644, 0.576, 0.507, 0.444, 0.380, 0.324, 0.273, 0.228, 0.188, 0.152, 0.121, 0.093, , 0.048, 0.029, 0.014, 0.000) from surface were used. These eta levels are used in this study because of their better representation of the topography (Routray et al., 2010) Evapotranspiration estimation The ET o from WRF downscaled meteorological and observation stations variables is calculated using the Penman and Monteith (PM) method proposed and developed by (Penman, 1956; Monteith, 1965) as given in FAO56 report (Allen et al., 1998). The ET o (in mm) according to the PM equation is as follows: ET o = (R n G) + γ 37 T +273 U 2 (e s e a ) ( ) (2) + γ 1 + r c r a where is the slope of the saturated vapour pressure curve (kpa C 1 ); R n the net radiation at the crop surface (MJ m 2 h 1 ); G the soil heat flux density (MJ m 2 h 1 ); γ the psychrometric constant (kpa C 1 ); T the mean air temperature at 2 m height ( C); e s the saturation vapour pressure (kpa); e a the actual vapour pressure (kpa); e s e a the saturation vapour pressure deficit (kpa); U 2 the wind speed at 2 m height m s 1 ; r a (aerodynamic resistance) = 208/U 2 sm 1 ;andr c (canopy resistance) = 70 s m Performance analysis The detailed investigation of weather variables derived from mesoscale model is compared to ground-based measurements. The estimated ET o from the NCEP and ECMWF is compared with in situ observations. The three performance statistics: the Nash-Sutcliffe efficiency (NSE) (Nash and Sutcliffe, 1970), Root Mean Square Error (RMSE) and Absolute Bias (Bias) are taken into account for performance measurements. The NSE is calculated using: NSE = 1 n [ ] 2 yi x i i=1 (3) n [x i x] 2 i=1 where x i is the ground-based measurements and y i is the estimated measurements.

4 NCEP and ECMWF comparison 121 The RMSE is calculated using the equation: ( ) RMSE = 1 n [ ] 2 yi x i n i=1 (4) The absolute bias (Bias) measures the positive or negative deviation of the measured value from the true value. The optimal value of Bias is 0.0, with low-magnitude values indicating accurate model simulation. It can be calculated using the following relation: Bias = [ (y x) ] (5) where x is the mean of ground-based measurements and y is the mean of estimated measurements. 3. Results and discussion 3.1. Performance of hydro-meteorological variables The WRF is simulated over the Brue catchment in order to calculate the hydro-meteorological variables. As discussed earlier, these calculations are made on an hourly basis representing dominant season in UK during the year The comparisons of the methods are first made on a seasonal basis and then represented on a combined form (pooled datasets). The observed seasonal and pooled weather variables performance statistics are shown in Table I. The three statistical indices, i.e. NSE, RMSE and Bias are calculated between the WRF downscaled weather variables (wind speed, dew point, surface temperature and solar radiation) and in situ measurements. It is seen that on a seasonal basis the ECMWF datasets are giving the smallest discrepancies as compared to the NCEP datasets derived variables. All the four weather variables from ECMWF have a RMSE much lower than the variable from NCEP. Weather variables downscaled from the ECMWF dataset have the minimum bias discrepancies. The modelled wind speed is generally greater than the measured during all the seasons under consideration. Previous studies by (Ishak et al., 2010; Ishak et al., 2013) also indicate that wind speed is the most difficult variable to downscale using the MM5 model with the results showing significant over estimation. On the basis of the NSE statistics, dew point and temperature have the highest values followed by least RMSE. Bias suggested a significant under estimation of radiation in nearly all seasons. Studies by Remesan et al. (2008) and Ahmadi et al. (2009) also suggested that solar radiation is a difficult parameter to obtain and has a high sensitivity towards ET o estimation (Ishak et al., 2010). In order to see the performances of the weather variables, the relative scatter plots are shown in Figure 2(a) (h). All the four seasons are giving good results as compared with the observed datasets. The worst case is the NCEP dataset, which has the largest deviation for equiline and showing a very poor NSE. Table I. Performance statistics for the seasonal and pooled hourly weather variables. January ECMWF NCEP Variables NSE RMSE Bias NSE RMSE Bias Dewpoint ( C) Temperature ( C) Wind speed (m s 1 ) Solar radiation (W m 2 ) April Dewpoint ( C) Temperature ( C) Wind speed (m s 1 ) Solar radiation (W m 2 ) July Dewpoint ( C) Temperature ( C) Wind speed (m s 1 ) Solar radiation (W m 2 ) October Dewpoint ( C) Temperature ( C) Wind speed (m s 1 ) Solar radiation (W m 2 ) Pooled Dewpoint ( C) Temperature ( C) Wind speed (m s 1 ) Solar radiation (W m 2 ) Comparative assessment of evapotranspiration products The relative plots between seasonal ECMWF and NCEP with observed ET o are shown in Figure 3(a) and (b), while the seasonal and pooled variation in ET o are depicted in Figure 3(c) (h). The performance statistics obtained between seasonal and pooled datasets are shown in Table II. A significant difference existed between the NCEP and ECMWF pooled ET o for all the four seasons under consideration. Two distinct features can be seen from these figures that ECMWF follows the seasonal variation better than the NCEP and secondly, a very high under estimation by NCEP from the ground observed datasets. The simulated and groundbased observation indicates that during the studied year, the ET o increases during the summer season following spring, and then decreased during the autumn and winter season, exhibiting a bell-shape response in the pooled datasets plots. Though the ET o increases from winter to summer, this increase is slightly more rapid in spring than other seasons. Bias statistics show that the simulated products, in general, under estimated the ground-based ET o. However, this under estimation is found to be very high for the NCEP data. The scatter plots obtained for NCEP pooled datasets with the observed ET o indicates a very high deviation from equiline, revealing a very high under estimation than ECMWF datasets. Again, in case of ECMWF all the four seasons are also giving good results as compared with the observed datasets. The worst case is the

5 122 P. K. Srivastava et al. Figure 2. (a h) Scatter plots representing the ECMWF (a d) and NCEP (e h) hourly weather variables.

6 NCEP and ECMWF comparison 123 (a) (b) (c) (d) (e) (f) (g) (h) Figure 3. (a h) Seasonal variations in the estimated ET o (a b); scatter plots representing the seasonal (c f) and pooled (g h) NCEP and ECMWF hourly ET o with observed datasets.

7 124 P. K. Srivastava et al. Table II. Performance statistics for the seasonal and pooled hourly ET o. Data Indicator January April July October Pooled ECMWF NSE RMSE Bias NCEP NSE RMSE Bias NCEP datasets, which has largest deviation for equiline and pitiable NSE observed for pooled one. During all seasons, in general, NCEP ET o under estimated the ground-based values. In contrast, a small over estimation is observed with the ECMWF datasets during spring and on the other hand autumn is showing an under estimation which seems to be associated with the rapid decrease in air temperature and radiation during this season. Conversely, the over estimations seems to correspond with the increases in air temperature and radiation, particularly during the spring. The RMSE in January is generally lesser than the corresponding values in other seasons, suggesting an influence of climatic variables on model downscaling. The magnitudes of RMSE indicate that ECMWF performed best during all the seasons and the worst performance is observed by NCEP datasets. The NSE values ranged from to NSE values are positive confirming that all the seasons are giving good agreement with the observed datasets for ET o except for October. However, for NCEP datasets the range of NSE is found to be from to 0.340, which is found to be very small as compared to ECMWF. The most probable reason for the lower efficiency of NCEP ET o can be attributed to the poor performances of wind and temperature. The high value of NSE obtained for ECMWF shows that ECMWF unambiguously performing better than NCEP and could be used for hydro-meteorological applications. 4. Conclusion A numerical weather model such as the WRF is able to downscale the global data into much finer resolutions in space and time for hydro-meteorological investigations. However, despite the importance of this valuable data source, there is lack of study in technical literature domain about the quality of such data. The downscaling process generally improves the data quality and provides higher data resolution. The study indicates that downscale products from the global reanalysis data to finer resolutions could be suitable for hydrological and meteorological applications. The ET o values estimated from the NCEP data are significantly under estimated across all the seasons. The suitability of data for ET o estimations suggest that ECMWF is giving far better performance than NCEP products. This study provides hydrologists with valuable information on downscaled weather variables from global datasets, and further exploration of this potentially valuable data source by the hydrological community should be encouraged so that useful experience and knowledge could be accumulated for different geographical and climatic conditions. A clear pattern is obtained among some weather variables with in situ measurements and seems to be promising for bias corrections in the downscaled data from in situ measurements or through regionalization from surrounding weather stations. Hence future research will focus on bias correction of theses global data for improved forecasting. Acknowledgements The authors would like to thank the Commonwealth Scholarship Commission, British Council, UK and Ministry of Human Resource Development, Government of India for providing the necessary support and funding for this research. The authors would like to acknowledge the British Atmospheric Data Centre, UK for providing the ground datasets. The author also acknowledges the Advanced Computing Research Centre at University of Bristol for providing the access to supercomputer facility (The Blue Crystal) for some of the analysis. References Ahmadi A, Han D, Karamouz M, Remesan R Input data selection for solar radiation estimation. 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