Evaluation of ensemble NWP models for dynamical downscaling of air temperature over complex topography in a hot climate: A case study from the Sultanate of Oman
Yassine Charabi
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Department of Geography, Sultan Qaboos University, Oman
Sultan Al-Yahyai
Department of Information Technology, Mazoon Electricity Company, Oman
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The solid line with a solid circle represents the NN predicted value. The dotted line with a solid square represents the actual observation. The boldface dashed line with a solid triangle represents the MLR model predicted value.</p>" ] ] ] "autores" => array:2 [ 0 => array:2 [ "autoresLista" => "Sankar Nath, S.D. Kotal" "autores" => array:2 [ 0 => array:2 [ "nombre" => "Sankar" "apellidos" => "Nath" ] 1 => array:2 [ "nombre" => "S.D." "apellidos" => "Kotal" ] ] ] 1 => array:2 [ "autoresLista" => "P.K. kundu" "autores" => array:1 [ 0 => array:2 [ "nombre" => "P.K." "apellidos" => "kundu" ] ] ] ] ] "idiomaDefecto" => "en" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S0187623617300115?idApp=UINPBA00004N" "url" => "/01876236/0000002800000004/v1_201703180150/S0187623617300115/v1_201703180150/en/main.assets" ] "itemAnterior" => array:19 [ "pii" => "S0187623617300097" "issn" => "01876236" "doi" => "10.20937/ATM.2015.28.04.04" "estado" => "S300" "fechaPublicacion" => "2015-10-01" "aid" => "73857" "copyright" => "Universidad Nacional Autónoma de México" "documento" => "article" "crossmark" => 0 "licencia" => "http://creativecommons.org/licenses/by-nc-nd/4.0/" "subdocumento" => "fla" "cita" => "Atmósfera. 2015;28:251-60" "abierto" => array:3 [ "ES" => true "ES2" => true "LATM" => true ] "gratuito" => true "lecturas" => array:2 [ "total" => 535 "formatos" => array:3 [ "EPUB" => 34 "HTML" => 348 "PDF" => 153 ] ] "en" => array:11 [ "idiomaDefecto" => true "titulo" => "Dust emission from different soil types in the northwest and the Indo-Gangetic Plains of India" "tienePdf" => "en" "tieneTextoCompleto" => "en" "tieneResumen" => array:2 [ 0 => "es" 1 => "en" ] "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "251" "paginaFinal" => "260" ] ] "contieneResumen" => array:2 [ "es" => true "en" => true ] "contieneTextoCompleto" => array:1 [ "en" => true ] "contienePdf" => array:1 [ "en" => true ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0015" "etiqueta" => "Fig. 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr3.jpeg" "Alto" => 778 "Ancho" => 1616 "Tamanyo" => 184394 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">(a) Global surface roughness. (b) Surface roughness over India. The threshold friction velocity at which dust particles are emitted from the surface in Jaisalmer, Bikaner and Jaipur is 0.27, 0.24 and 0.23 m/s, respectively.</p>" ] ] ] "autores" => array:3 [ 0 => array:2 [ "autoresLista" => "Nirmala D. Desouza" "autores" => array:1 [ 0 => array:2 [ "nombre" => "Nirmala D." "apellidos" => "Desouza" ] ] ] 1 => array:2 [ "autoresLista" => "D. Blaise" "autores" => array:1 [ 0 => array:2 [ "nombre" => "D." "apellidos" => "Blaise" ] ] ] 2 => array:2 [ "autoresLista" => "Rajnish Kurchania, M.S. Qureshi" "autores" => array:2 [ 0 => array:2 [ "nombre" => "Rajnish" "apellidos" => "Kurchania" ] 1 => array:2 [ "nombre" => "M.S." "apellidos" => "Qureshi" ] ] ] ] ] "idiomaDefecto" => "en" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S0187623617300097?idApp=UINPBA00004N" "url" => "/01876236/0000002800000004/v1_201703180150/S0187623617300097/v1_201703180150/en/main.assets" ] "en" => array:18 [ "idiomaDefecto" => true "titulo" => "Evaluation of ensemble NWP models for dynamical downscaling of air temperature over complex topography in a hot climate: A case study from the Sultanate of Oman" "tieneTextoCompleto" => true "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "261" "paginaFinal" => "269" ] ] "autores" => array:2 [ 0 => array:4 [ "autoresLista" => "Yassine Charabi" "autores" => array:1 [ 0 => array:4 [ "nombre" => "Yassine" "apellidos" => "Charabi" "email" => array:1 [ 0 => "yassine@squ.edu.om" ] "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">*</span>" "identificador" => "cor0005" ] ] ] ] "afiliaciones" => array:1 [ 0 => array:2 [ "entidad" => "Department of Geography, Sultan Qaboos University, Oman" "identificador" => "aff0005" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "*" "correspondencia" => "Corresponding author." ] ] ] 1 => array:3 [ "autoresLista" => "Sultan Al-Yahyai" "autores" => array:1 [ 0 => array:2 [ "nombre" => "Sultan" "apellidos" => "Al-Yahyai" ] ] "afiliaciones" => array:1 [ 0 => array:2 [ "entidad" => "Department of Information Technology, Mazoon Electricity Company, Oman" "identificador" => "aff0010" ] ] ] ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0025" "etiqueta" => "Fig. 5" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr5.jpeg" "Alto" => 2395 "Ancho" => 1533 "Tamanyo" => 311849 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">Verification results for the Salalah station scatter plots for different forecasts: (a) T C-HRM-GME; (b) T C-HRM-ECMWF; (c) T C-COSMO-GME); (d) T C-COSMO-EC-MWF; (e) T C-Ensemble Mean); (f) Comparison of ensemble members, ensemble mean and observed monthly mean time series).</p>" ] ] ] "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">1</span><span class="elsevierStyleSectionTitle" id="sect0020">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">Over the last decade, the availability of increasing computing power has supported concerted efforts to improve the resolution of numerical weather prediction (NWP) models (<a class="elsevierStyleCrossRef" href="#bib0110">Ruby Leung <span class="elsevierStyleItalic">et al.</span>, 2003</a>). Despite these efforts, the resolution of NWPs remains coarse. The typical resolution ranges from a few dozen kilometers for general circulation models (GCMs) down to a few kilometers for limited-area models (LAMs) (<a class="elsevierStyleCrossRef" href="#bib0035">Eccel <span class="elsevierStyleItalic">et al.</span>, 2007</a>). The air temperature at 2 m above the ground is one of the main meteorological parameters forecasted by NWP models, but this prediction is closely tied to the topographic position assigned by the model to each grid point. Air temperature is strongly affected by topography, and large-scale models can be a source of strong bias in complex terrain. The lowest model layer is much higher than 2 m, adding to the bias introduced by the horizontal resolution. Therefore, the 2 m temperature is not a prognostic model variable but is interpolated from the lowest model layer. The type of interpolation will contribute to the bias in the forecasted 2 m temperature compared with the measured one. Therefore, a downscaling approach is used as a post-processing step for deriving finer resolution information from large-scale NWP models and to relate grid point predictions to the actual physical sites (<a class="elsevierStyleCrossRef" href="#bib0065">Hewitson and Crane, 1996</a>; <a class="elsevierStyleCrossRef" href="#bib0035">Eccel <span class="elsevierStyleItalic">et al.</span>, 2007</a>).</p><p id="par0010" class="elsevierStylePara elsevierViewall">There are two principal types of downscaling techniques: statistical/probabilistic and dynamical. Statistical/probabilistic downscaling methods use historical data and archived forecasts to generate downscaled data from large-scale forecasts (<a class="elsevierStyleCrossRef" href="#bib0095">Murphy, 1998</a>; <a class="elsevierStyleCrossRef" href="#bib0115">Rummukainen, 2010</a>). Statistical downscaling consists in identifying empirical links between large-scale patterns of climate elements (predictors) and local climate (the predictands), and applying them to output from global or regional models. This approach is very simple to implement, fits well with areas with large datasets and generates consistent estimates for periods similar to those used for their calibration. Successful downscaling depends on long, reliable observational series of predictors and predictands.Dynamical downscaling methods encompass dynamic models of the atmosphere nested within the grids of the large-scale forecast models. Typically, one-way nested LAMs are implemented to generate finer resolutions for different applications, with GCMs providing initial and lateral boundary conditions. Dynamic downscaling has become very popular; the physical model formulation offers strong justification for its application under a variety of climate and weather conditions, particularly for locations with strong boundary forcing, such as complex terrain with irregular orography (<a class="elsevierStyleCrossRef" href="#bib0095">Murphy, 1998</a>; <a class="elsevierStyleCrossRef" href="#bib0115">Rummukainen, 2010</a>). The disadvantage ofthis approach is related to the high computational cost and data requirements (e.g., three dimensional boundary and initial conditions).</p><p id="par0015" class="elsevierStylePara elsevierViewall">Statistical and dynamic downscaling techniques have been used, separately or combined, in meteorology and hydrology to improve understanding of local climate variability (<a class="elsevierStyleCrossRef" href="#bib0005">Al-Yahyai <span class="elsevierStyleItalic">et al.</span>, 2011</a>; <a class="elsevierStyleCrossRef" href="#bib0010">Burger, 1996</a>; <a class="elsevierStyleCrossRef" href="#bib0050">Fowler <span class="elsevierStyleItalic">et al.</span>, 2007</a>; <a class="elsevierStyleCrossRef" href="#bib0045">Fuentes and Heimann, 2000</a>; <a class="elsevierStyleCrossRef" href="#bib0060">Haas and Born, 2011</a>; <a class="elsevierStyleCrossRef" href="#bib0070">Hubener and Kerschgens, 2007</a>; <a class="elsevierStyleCrossRef" href="#bib0075">Kidson and Thompson, 1998</a>; <a class="elsevierStyleCrossRef" href="#bib0085">Maraun <span class="elsevierStyleItalic">et al.</span>, 2010</a>; <a class="elsevierStyleCrossRef" href="#bib0090">Michelangeli <span class="elsevierStyleItalic">et al.</span>, 2009</a>; <a class="elsevierStyleCrossRef" href="#bib0105">Pinto <span class="elsevierStyleItalic">et al.</span>, 2010</a>; <a class="elsevierStyleCrossRef" href="#bib0140">Wilby <span class="elsevierStyleItalic">et al.</span>, 1998</a>; Wilby and Wigley, 1997). Ensemble approaches were introduced in meteorology (<a class="elsevierStyleCrossRef" href="#bib0055">Galmarini <span class="elsevierStyleItalic">et al</span>., 2001</a>) and hydrology (<a class="elsevierStyleCrossRef" href="#bib0120">Stedinger and Kim, 2009</a>) to improve model forecasts and reduce the model uncertainty. Any group of model forecasts with the same valid time is called an ensemble (<a class="elsevierStyleCrossRef" href="#bib0135">UCAR, 2009</a>), and each forecast is called an ensemble member. The extent of agreement among the members can be considered a measure of forecast certainty (<a class="elsevierStyleCrossRef" href="#bib0125">Stensrud <span class="elsevierStyleItalic">et al.</span>, 1999</a>). Ensemble forecasting can quantify and propagate forecast uncertainty (<a class="elsevierStyleCrossRef" href="#bib0130">Tiwari and Chatteijee, 2010</a>; <a class="elsevierStyleCrossRef" href="#bib0100">National Research Council, 2006</a>).</p><p id="par0020" class="elsevierStylePara elsevierViewall">The implementation of downscaling techniques in developing countries poses a real challenge due to the modest computational infrastructure. The main motivation of this study is to construct an ensemble of forecasts over a complex, hot area. Several techniques for constructing the ensemble have been developed and exhibit better performance than any single model system (<a class="elsevierStyleCrossRef" href="#bib0015">Callado <span class="elsevierStyleItalic">et al.</span>, 2013</a>). The proposed methodology suggests using various sources of NWP model uncertainty as a starting point to generate an ensemble of NWP predictions for temperature data. This method can be achieved by using different LAMs (initial/ boundary) derived by different GCMs. The Sultanate of Oman, characterized by a hyper-arid climate because of its position astride the tropic of Cancer, was selected as a reference area for this application (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>). The rainfall regime, the teleconnections and the wet- and dry-spell patterns have been analyzed on a regional scale in this region (Charabi, 2009; <a class="elsevierStyleCrossRef" href="#bib0020">Charabi and Hatrushi, 2010</a>; <a class="elsevierStyleCrossRef" href="#bib0025">Charabi and Al-Yahyai, 2011</a>). The studies have shown that the area is influenced by<a name="p3"></a> different atmospheric mechanisms which contribute to local climate diversity and that are characterized by a strong gradient of temperature induced by the complexity of the topography. There are no studies of this region that address the interaction between such complex topography and temperature at local scales. The outline of the paper is as follows: section 2 details the proposed approach; section 3 discusses the main findings based on a case study over Oman; and section 4 concludes the paper.</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2</span><span class="elsevierStyleSectionTitle" id="sect0025">Data sets and methodology</span><p id="par0025" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#fig0010">Figure 2</a> gives a comprehensive overview of the ensemble NWP model approach for dynamical downscaling of temperature. It shows that initial and lateral boundary conditions from [M] GCMs (40<span class="elsevierStyleHsp" style=""></span>×<span class="elsevierStyleHsp" style=""></span>40<span class="elsevierStyleHsp" style=""></span>km) are used to derive and initialize [N] LAMs (7<span class="elsevierStyleHsp" style=""></span>×<span class="elsevierStyleHsp" style=""></span>7<span class="elsevierStyleHsp" style=""></span>km). This permutation generates an ensemble system [M<span class="elsevierStyleHsp" style=""></span>×<span class="elsevierStyleHsp" style=""></span>N] member. In this combination, each LAM will be initialized by [M] different GCMs. The models are equally weighted, meaning the ensemble members for each model are calculated first and then averaged to form the multi-model mean. This regional scale ensemble prediction is validated with the ground observations and used to derive and initialize a local scale high-resolution model (2.8<span class="elsevierStyleHsp" style=""></span>×<span class="elsevierStyleHsp" style=""></span>2.8<span class="elsevierStyleHsp" style=""></span>km). Notice that the number of ensemble members is controlled by the availability of the GCMs data and the computational power. The higher the number of members, the more confident the derived statistics but the more computational power required (<a class="elsevierStyleCrossRef" href="#bib0005">Al-Yahyai <span class="elsevierStyleItalic">et al.</span>, 2011</a>). For this application, an ensemble system was constructed using four members and covering the domain 49.0-64.0° E and 13.0-28.0° N with a 7<span class="elsevierStyleHsp" style=""></span>km resolution with 241 x 241 grid points and 41 vertical layers. Two LAMs, namely the high-resolution model (HRM), which is hydrostatic (<a class="elsevierStyleCrossRef" href="#bib0080">Majewski, 2009</a>), and the model from the Consortium for Small-Scale Modeling (COSMO), which is non-hydrostatic (<a class="elsevierStyleCrossRef" href="#bib0030">Doms and Schattler, 2008</a>), formed the ensemble members.</p><elsevierMultimedia ident="fig0010"></elsevierMultimedia><p id="par0030" class="elsevierStylePara elsevierViewall">Each model run is initialized at 00:00 UTC and generates output for 30 hours. The first six hours are discarded due to the spin-up of the model. The two LAMs are derived and initialized by the GCMs’ data from the German Global Model (GME), which runs at 40<span class="elsevierStyleHsp" style=""></span>km resolution using two different initial states of the atmosphere. The first initial state of the atmosphere is provided by the 3Dvar data assimilation system at the German Weather Service (Deutscher Wetterdienst, DWD), and the second initial state is provided from the reanalysis data ERA-Interim from the European Centre for Medium-Range Weather Forecasts (<a class="elsevierStyleCrossRef" href="#bib0040">ECMWF, 2006</a>). The HRM model has been an operational model at the Directorate General of Meteorology and Air Navigation (DGMAN), Oman, since 1999. The COSMO model has recently been implemented as a test bed under the research agreement between DGMAN and DWD. Through this cooperative agreement, DWD provided the initial and lateral boundary conditions for 2009 for this study; the study therefore covers only 2009. The PC cluster of the DGMAN was used to run the case study after the operational runs of its operational models.<a name="p4"></a></p></span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">3</span><span class="elsevierStyleSectionTitle" id="sect0030">Results and discussion</span><p id="par0035" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#fig0015">Figure 3</a> shows the annual mean temperature at 2 m above the ground from the four ensemble members. This figure shows the uncertainty of the NWP models<a name="p5"></a> and illustrates the effects ofmodel dynamics, numerical schemes and the initial conditions. The HRM-DWD and COSMO-DWD maps clearly highlight the effect of the model dynamic and numerical schemes. The two LAMs are initialized with the same atmospheric states engendered in two different forecasts. The effect of the initial state is clearly shown, with maps of the same model (e.g. HRM) using different initial states engendering two different forecasts. Notice that the effects of the model dynamics and numerical schemes are more pronounced than the effect of the initial state. It can be seen that the HRM model is more sensitive to the initial and boundary condition data than the COSMO model. The HRM model initialized by the DWD 3D data assimilation produced high temperatures over the Empty Quarter desert.</p><elsevierMultimedia ident="fig0015"></elsevierMultimedia><p id="par0040" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#fig0020">Figure 4</a> shows the annual ensemble mean of the system. The ensemble mean smoothed out the unpredictable events, such as the high temperature over the Empty Quarter desert. On the other hand, the more predictable events, such as low air temperature over the mountains, were maintained in the ensemble mean.</p><elsevierMultimedia ident="fig0020"></elsevierMultimedia><p id="par0045" class="elsevierStylePara elsevierViewall">Eleven meteorological stations were selected to verify the robustness of the temperature simulation of the four ensemble members and the ensemble mean against ground observations. <a class="elsevierStyleCrossRef" href="#fig0025">Figure 5</a> shows the scatter plot for different forecasts over Salalah. In most cases, the model underestimated the low temperature values and overestimated the high temperature values. Therefore, the NWP models have warm and cold biases over Salalah. Due to the variation in bias of the models, the ensemble mean showed outliers in the scatter plot. Monthly time series of the mean temperature were also computed. The diagram at the lower right corner shows the model validation data over Salalah against the observation data (black curve). All models underestimated the temperature during winter and overestimated the temperature during summer. The model discrimination over Salalah is believed to be related to the complex terrain surrounding the observational station and the large seasonal temperature variation. The southern part of Oman, where Salalah is located, is influenced by the Arabian summer monsoon from June to September, which considerably reduces the temperature. Compared with Salalah, the scatter plot over Sohar shows a better correlation with the measurement observations, as shown in <a class="elsevierStyleCrossRef" href="#fig0030">Figure 6</a>. Similar results from the ensemble mean model were observed for the other stations.</p><elsevierMultimedia ident="fig0025"></elsevierMultimedia><elsevierMultimedia ident="fig0030"></elsevierMultimedia><p id="par0050" class="elsevierStylePara elsevierViewall">The mean error (bias) of the four ensemble members and the ensemble mean is calculated as described by <a class="elsevierStyleCrossRef" href="#eq0005">Eq. (1)</a>:<elsevierMultimedia ident="eq0005"></elsevierMultimedia></p><p id="par0055" class="elsevierStylePara elsevierViewall">where F is the model forecast, O is the observation and N is the total number of data sets.</p><p id="par0060" class="elsevierStylePara elsevierViewall">The mean error is determined from the means of the closest points of the model to an observational station and the bi-linearly interpolated value of the four surrounding points. <a class="elsevierStyleCrossRef" href="#fig0035">Figure 7</a> shows the mean error (bias) of the four ensemble members, the mean error of the ensemble mean using the closest grid point approach and the bi-linear interpolation of the four surrounding grid points for eleven meteorological stations. It clearly shows that all members are overestimating the temperature for all stations by 1-3.5 °C. The highest overestimation is shown in Buraimi and Ibri. This significant difference can be explained by the topographic divergence between<a name="p6"></a><a name="p7"></a><a name="p8"></a> the elevation of the station and the elevation perceived by the model. For these two stations, the difference is more than 100 m. Moreover, these differences in temperatures may be related to planetary boundary layer (PBL) heights. PBL heights are underestimated in those regions, which may be a result of differences in land cover between our downscaling models data set and the ground-truth data. Furthermore, Burimi and Ibri are highly influenced by the strong sea breeze blowing from the northeast coast of the United Arab Emirates. This deep penetration of sea breezes over a large flat area contributes to a reduction in the air temperature (<a class="elsevierStyleCrossRef" href="#bib0025">Charabi and Al-Yahyai, 2011</a>).</p><elsevierMultimedia ident="fig0035"></elsevierMultimedia><p id="par0065" class="elsevierStylePara elsevierViewall">Among the four members and the mean error of the ensemble mean, HRM-GME performed better for Duqum; HRM-ECMWF performed better in the case of Sohar; and Ibra, Ibri and As Seeb were forecasted better by COSMO-GME. Adam, Buraimi, Masirah, Nizwa, and Salalah were forecasted better by the ensemble mean. These results highlight the uncertainty of the NWP model and show that there is no best model for the entire domain. The bi-linear approach indicated that the ensemble mean performed better for six stations.</p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">4</span><span class="elsevierStyleSectionTitle" id="sect0035">Conclusion</span><p id="par0070" class="elsevierStylePara elsevierViewall">This paper assesses the use of ensemble NWP models for dynamical downscaling of temperature over a complex hot area. The results show the uncertainty in temperature prediction due to the uncertainties in the NWP models that were used and indicate that there is no best model for the entire domain. The NWP models performed relatively poorly in predicting temperature; this is mainly because the NWP models reliance on simple soil physics is insufficient to capture the temperature cycle over the different topographic settings. The multilayer soil model used in NWP models mainly simulates soil temperature evolution; soil vegetation, humidity and canopy are based on seasonal variations in land cover and are not explicitly computed. In Oman, accurate topographical information and advanced surface physics are required to improve temperature prediction. Therefore, soil hydrological models and plant canopy models are important for realistic assessments of evaporation, evapotranspiration and their impacts on the latent and sensible surface heat fluxes that directly influence the air temperature.</p><p id="par0075" class="elsevierStylePara elsevierViewall">¡The ensemble mean performed better on average than individual members. The atmosphere is a chaotic system, where predictability is lost in a manner-dependent flow; providing a single control forecast is of limited use. An ensemble approach, where multiple predictions are generated through initial and model perturbations, can be used to assess variations in predictability and substantially reduce the uncertainty.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:9 [ 0 => array:3 [ "identificador" => "xres816083" "titulo" => "Resumen" "secciones" => array:1 [ 0 => array:1 [ "identificador" => "abst0005" ] ] ] 1 => array:3 [ "identificador" => "xres816084" "titulo" => "Abstract" "secciones" => array:1 [ 0 => array:1 [ "identificador" => "abst0010" ] ] ] 2 => array:2 [ "identificador" => "xpalclavsec813303" "titulo" => "Keywords" ] 3 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 4 => array:2 [ "identificador" => "sec0010" "titulo" => "Data sets and methodology" ] 5 => array:2 [ "identificador" => "sec0015" "titulo" => "Results and discussion" ] 6 => array:2 [ "identificador" => "sec0020" "titulo" => "Conclusion" ] 7 => array:2 [ "identificador" => "xack273905" "titulo" => "Acknowledgments" ] 8 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2014-01-16" "fechaAceptado" => "2015-08-05" "PalabrasClave" => array:1 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec813303" "palabras" => array:4 [ 0 => "Dynamical downscaling" 1 => "ensemble NWP models" 2 => "temperature" 3 => "complex hot area" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "es" => array:2 [ "titulo" => "Resumen" "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Este trabajo evalúa el uso de modelos numéricos de predicción del tiempo (NWP, por sus siglas en inglés) por conjuntos para la reducción dinámica de escala de la temperatura en una región cálida y compleja. Este enfoque ofrece información sobre la incertidumbre de los modelos NWP y proporciona información probabilística para compararlos con los modelos NWP sencillos que se utilizan en la actualidad. Se construyó un sistema por conjuntos utilizando cuatro partes con una resolución de 7<span class="elsevierStyleHsp" style=""></span>km sobre Omán. Dichas partes estuvieron conformadas por dos modelos de área limitada (LAM, por sus siglas en inglés), el modelo de alta resolución y el modelo del Consorcio para la Modelación de Pequeña Escala. Los dos LAM se derivaron e inicializaron utilizando datos del modelo de circulación general del modelo global alemán, que opera con una resolución de 40<span class="elsevierStyleHsp" style=""></span>km con base en dos estados atmosféricos iniciales. El primer estado inicial fue proporcionado por el sistema de asimilación de datos 3Dvar del Servicio Meteorológico Alemán, y el segundo estado inicial se obtuvo a partir de los datos de reanálisis (ERA-Interim) del Centro Europeo para la Predicción del Tiempo a Plazo Medio. Los resultados manifiestan una incertidumbre en la predicción de la temperatura relacionada con la incertidumbre de los modelos NWP utilizados, e indican que no hay un modelo idóneo para la totalidad del dominio. En general, el promedio del conjunto tuvo un mejor desempeño que las partes individuales.</p></span>" ] "en" => array:2 [ "titulo" => "Abstract" "resumen" => "<span id="abst0010" class="elsevierStyleSection elsevierViewall"><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">This paper evaluates the use of ensemble numerical weather prediction (NWP) models for dynamical downscaling of temperature over a complex, hot region. This approach delivers information about the uncertainty of the NWP models and provides probabilistic information for comparison with the currently used single NWP model. An ensemble system was constructed using four members with a 7<span class="elsevierStyleHsp" style=""></span>km resolution over Oman. Two limited-area models (LAMs), the high-resolution model (HRM) and the model from the Consortium for Small-Scale Modeling (COSMO) formed the ensemble members. The two LAMs were derived and initialized using the general circulation model (GCM) data from the German Global Model (GME), which runs at 40<span class="elsevierStyleHsp" style=""></span>km resolution, using two different initial atmospheric states. The first initial state was provided by the 3Dvar data assimilation system at the German Weather Service (Deutscher Wetterdienst, DWD), and the second initial state was provided from the reanalysis data (ERA-Interim) from the European Centre for Medium-Range Weather Forecasts (ECMWF). The results reveal the uncertainty in temperature prediction related to the uncertainty of the NWP models that were used and indicate that there is no best model for the entire domain. On average, the ensemble mean performed better than individual members.<a name="p2"></a></p></span>" ] ] "multimedia" => array:8 [ 0 => array:7 [ "identificador" => "fig0005" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 1026 "Ancho" => 940 "Tamanyo" => 162047 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">2.8<span class="elsevierStyleHsp" style=""></span>km averaged elevation (m) of the study area.</p>" ] ] 1 => array:7 [ "identificador" => "fig0010" "etiqueta" => "Fig. 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 582 "Ancho" => 948 "Tamanyo" => 83001 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">Diagram of the ensemble NWP model approach for dynamical downscaling of temperature.</p>" ] ] 2 => array:7 [ "identificador" => "fig0015" "etiqueta" => "Fig. 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr3.jpeg" "Alto" => 2301 "Ancho" => 1419 "Tamanyo" => 705713 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">Annual mean temperature at 2 m above the ground from the four ensemble members. (a) COSMO-ECMWF; (b) HRM-ECMWF; (c) COSMO-GME; (d) HRM-GME.</p>" ] ] 3 => array:7 [ "identificador" => "fig0020" "etiqueta" => "Fig. 4" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr4.jpeg" "Alto" => 1086 "Ancho" => 693 "Tamanyo" => 187719 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Annual mean temperature at 2 m above the ground from the ensemble mean.</p>" ] ] 4 => array:7 [ "identificador" => "fig0025" "etiqueta" => "Fig. 5" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr5.jpeg" "Alto" => 2395 "Ancho" => 1533 "Tamanyo" => 311849 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">Verification results for the Salalah station scatter plots for different forecasts: (a) T C-HRM-GME; (b) T C-HRM-ECMWF; (c) T C-COSMO-GME); (d) T C-COSMO-EC-MWF; (e) T C-Ensemble Mean); (f) Comparison of ensemble members, ensemble mean and observed monthly mean time series).</p>" ] ] 5 => array:7 [ "identificador" => "fig0030" "etiqueta" => "Fig. 6" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr6.jpeg" "Alto" => 2403 "Ancho" => 1503 "Tamanyo" => 309854 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">Verification results for the Sohar station scatter plots for different forecasts. (a) T C-HRM-GME); (b) T C-HRM-ECMWF); (c) T C-COSMO-GME); (d) T C-COSMO-EC- MWF); (e) T C-ensemble mean); (f) comparison of ensemble members, ensemble mean and observed monthly mean time series.</p>" ] ] 6 => array:7 [ "identificador" => "fig0035" "etiqueta" => "Fig. 7" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr7.jpeg" "Alto" => 703 "Ancho" => 943 "Tamanyo" => 101065 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Mean error (bias) of the ensemble members at different locations.</p>" ] ] 7 => array:6 [ "identificador" => "eq0005" "etiqueta" => "(1)" "tipo" => "MULTIMEDIAFORMULA" "mostrarFloat" => false "mostrarDisplay" => true "Formula" => array:5 [ "Matematica" => "Bias=1N∑i=1NFi−Oi" "Fichero" => "STRIPIN_si1.jpeg" "Tamanyo" => 1720 "Alto" => 49 "Ancho" => 141 ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0005" "bibliografiaReferencia" => array:28 [ 0 => array:3 [ "identificador" => "bib0005" "etiqueta" => "Al-Yahyai et al., 2011" "referencia" => array:1 [ 0 => array:3 [ "comentario" => "doi:10.1016/j.renene.2011.06.014" "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Nested ensemble NWP approach for wind energy assessment" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:4 [ 0 => "S. 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| Year/Month | Html | Total | |
|---|---|---|---|
| 2024 November | 7 | 0 | 7 |
| 2024 October | 18 | 3 | 21 |
| 2024 September | 35 | 14 | 49 |
| 2024 August | 36 | 3 | 39 |
| 2024 July | 23 | 3 | 26 |
| 2024 June | 29 | 3 | 32 |
| 2024 May | 10 | 2 | 12 |
| 2024 April | 18 | 2 | 20 |
| 2024 March | 28 | 2 | 30 |
| 2024 February | 42 | 6 | 48 |
| 2024 January | 31 | 5 | 36 |
| 2023 December | 35 | 6 | 41 |
| 2023 November | 43 | 12 | 55 |
| 2023 October | 28 | 13 | 41 |
| 2023 September | 53 | 7 | 60 |
| 2023 August | 39 | 3 | 42 |
| 2023 July | 18 | 6 | 24 |
| 2023 June | 13 | 3 | 16 |
| 2023 May | 51 | 11 | 62 |
| 2023 April | 44 | 2 | 46 |
| 2023 March | 48 | 1 | 49 |
| 2023 February | 32 | 8 | 40 |
| 2023 January | 15 | 9 | 24 |
| 2022 December | 12 | 7 | 19 |
| 2022 November | 30 | 6 | 36 |
| 2022 October | 14 | 10 | 24 |
| 2022 September | 30 | 15 | 45 |
| 2022 August | 26 | 9 | 35 |
| 2022 July | 18 | 9 | 27 |
| 2022 June | 14 | 9 | 23 |
| 2022 May | 15 | 7 | 22 |
| 2022 April | 15 | 9 | 24 |
| 2022 March | 44 | 14 | 58 |
| 2022 February | 27 | 6 | 33 |
| 2022 January | 67 | 6 | 73 |
| 2021 December | 43 | 16 | 59 |
| 2021 November | 27 | 9 | 36 |
| 2021 October | 41 | 16 | 57 |
| 2021 September | 14 | 11 | 25 |
| 2021 August | 21 | 7 | 28 |
| 2021 July | 13 | 8 | 21 |
| 2021 June | 16 | 6 | 22 |
| 2021 May | 20 | 8 | 28 |
| 2021 April | 44 | 4 | 48 |
| 2021 March | 43 | 8 | 51 |
| 2021 February | 42 | 8 | 50 |
| 2021 January | 45 | 10 | 55 |
| 2020 December | 30 | 7 | 37 |
| 2020 November | 16 | 9 | 25 |
| 2020 October | 12 | 5 | 17 |
| 2020 September | 13 | 10 | 23 |
| 2020 August | 14 | 10 | 24 |
| 2020 July | 4 | 2 | 6 |
| 2020 June | 6 | 1 | 7 |
| 2020 May | 12 | 4 | 16 |
| 2020 April | 6 | 5 | 11 |
| 2020 March | 14 | 7 | 21 |
| 2020 February | 8 | 5 | 13 |
| 2020 January | 15 | 7 | 22 |
| 2019 December | 19 | 11 | 30 |
| 2019 November | 9 | 3 | 12 |
| 2019 October | 8 | 2 | 10 |
| 2019 September | 9 | 2 | 11 |
| 2019 August | 5 | 1 | 6 |
| 2019 July | 4 | 27 | 31 |
| 2019 June | 28 | 23 | 51 |
| 2019 May | 97 | 49 | 146 |
| 2019 April | 50 | 5 | 55 |
| 2019 March | 0 | 4 | 4 |
| 2019 February | 5 | 2 | 7 |
| 2019 January | 3 | 2 | 5 |
| 2018 December | 5 | 4 | 9 |
| 2018 November | 13 | 6 | 19 |
| 2018 October | 10 | 6 | 16 |
| 2018 September | 0 | 5 | 5 |
| 2018 August | 5 | 0 | 5 |
| 2018 July | 0 | 1 | 1 |
| 2018 June | 2 | 0 | 2 |
| 2018 May | 0 | 2 | 2 |
| 2018 April | 5 | 0 | 5 |
| 2018 March | 1 | 2 | 3 |
| 2018 January | 1 | 0 | 1 |
| 2017 December | 4 | 0 | 4 |
| 2017 November | 3 | 0 | 3 |
| 2017 October | 2 | 0 | 2 |
| 2017 September | 2 | 1 | 3 |
| 2017 August | 9 | 0 | 9 |
| 2017 July | 7 | 1 | 8 |
| 2017 June | 3 | 1 | 4 |
| 2017 May | 1 | 0 | 1 |
| 2017 April | 1 | 0 | 1 |
| 2017 March | 2 | 1 | 3 |
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