scholarly journals Wind Speed Seasonality in a Brazilian Amazon-Savanna Region from the Global Land Data Assimilation System

2020 ◽  
Vol 42 ◽  
pp. e12
Author(s):  
Leonardo Henrique De Sá Rodrigues ◽  
Marcos Aurélio Alves Freitas ◽  
Luan Victor Soares Pereira ◽  
Brunna Caroline Correia Dias ◽  
Vicente Marques Silvino ◽  
...  
Keyword(s):  
Wind Speed ◽  
Data Assimilation ◽  
Wind Energy ◽  
Linear Regression Analysis ◽  
The State ◽  
Global Land ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Assimilation System

The objective of this study was to develop a methodology for the use of remote sensing data for the planning of wind energy projects in Maranhão. Monthly wind speed and precipitation data from 2000 to 2016 were used. Initially, wind velocity data were processed using the principal component analysis (PCA) technique. Next, the grouping technique known as k-means was used. Finally, a linear regression analysis was performed with the objective of identifying the parameters to be used in the validation of the data estimated by the Global Land Data Assimilation System (GLDAS) base against the data measured by the meteorological stations. Four homogeneous zones were identified; the zone with the highest values of monthly average wind speeds is in the northern region of the state on the coast. The period of greatest intensity of the winds was identified to be in the months of October and November. The lowest values of precipitation were observed during these months. The analyses carried out by this study show a favorable scenario for the production of wind energy in the state of Maranhão.

From Monitoring to Forecasting the Land Surface Condition Using a Land Data Assimilation System: Application over the Contiguous United States

2020 ◽  
Author(s):  
Anthony Mucia ◽  
Clément Albergel ◽  
Bertrand Bonan ◽  
Yongjun Zheng ◽  
Jean-Christophe Calvet
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Initial Conditions ◽  
Global Land ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Assimilation System

<p>LDAS-Monde is a global Land Data Assimilation System developed in the research department of Météo-France (CNRM) to monitor Land Surface Variables (LSVs) at various scales, from regional to global. With LDAS-Monde, it is possible to assimilate satellite derived observations of Surface Soil Moisture (SSM) and Leaf Area Index (LAI) e.g. from the Copernicus Global Land Service (CGLS). It is an offline system normally driven by atmospheric reanalyses such as ECMWF ERA5.</p><p>In this study we investigate LDAS-Monde ability to use atmospheric forecasts to predict LSV states up to weeks in advance. In addition to the accuracy of the forecast predictions, the impact of the initialization on the LSVs forecast is addressed. To perform this study, LDAS-Monde is forced by a fifteen-day forecast from ECMWF for the 2017-2018 period over the Contiguous United States (CONUS) at 0.2<sup>o</sup> x 0.2<sup>o</sup> spatial resolution. These LSVs forecasts are initialized either by the model alone (LDAS-Monde open-loop, no assimilation, Fc_ol) or by the analysis (assimilation of SSM and LAI, Fc_an). These two sets of forecast are then assessed using satellite derived observations of SSM and LAI, evapotranspiration estimates, as well as in situ measurements of soil moisture from the U.S. Climate Reference Network (USCRN). Results indicate that for the three evaluation variables (SSM, LAI, and evapotranspiration), LDAS-Monde provides reasonably accurate predictions two weeks in advance. Additionally, the initial conditions are shown to make a positive impact with respect to LAI, evapotranspiration, and deeper layers of soil moisture when using Fc_an. Moreover, this impact persists in time, particularly for vegetation related variables. Other model variables (such as runoff and drainage) are also affected by the initial conditions. Future work will focus on the transfer of this predictive information from a research to stakeholder tool.</p>

scholarly journals Evaluation of the Global Land Data Assimilation System (GLDAS) Air Temperature Data Products

2015 ◽  
Vol 16 (6) ◽  
pp. 2463-2480 ◽  
Author(s):  
Lei Ji ◽  
Gabriel B. Senay ◽  
James P. Verdin
Keyword(s):  
Data Assimilation ◽  
Air Temperature ◽  
Temperature Data ◽  
Mountainous Areas ◽  
Global Land ◽  
Land Data Assimilation ◽  
Weather Stations ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Assimilation System

Abstract There is a high demand for agrohydrologic models to use gridded near-surface air temperature data as the model input for estimating regional and global water budgets and cycles. The Global Land Data Assimilation System (GLDAS) developed by combining simulation models with observations provides a long-term gridded meteorological dataset at the global scale. However, the GLDAS air temperature products have not been comprehensively evaluated, although the accuracy of the products was assessed in limited areas. In this study, the daily 0.25° resolution GLDAS air temperature data are compared with two reference datasets: 1) 1-km-resolution gridded Daymet data (2002 and 2010) for the conterminous United States and 2) global meteorological observations (2000–11) archived from the Global Historical Climatology Network (GHCN). The comparison of the GLDAS datasets with the GHCN datasets, including 13 511 weather stations, indicates a fairly high accuracy of the GLDAS data for daily temperature. The quality of the GLDAS air temperature data, however, is not always consistent in different regions of the world; for example, some areas in Africa and South America show relatively low accuracy. Spatial and temporal analyses reveal a high agreement between GLDAS and Daymet daily air temperature datasets, although spatial details in high mountainous areas are not sufficiently estimated by the GLDAS data. The evaluation of the GLDAS data demonstrates that the air temperature estimates are generally accurate, but caution should be taken when the data are used in mountainous areas or places with sparse weather stations.

scholarly journals CARACTERIZAÇÃO ESPAÇO-TEMPORAL DOS COMPONENTES DO BALANÇO DE RADIAÇÃO E CALOR NA REGIÃO DE TRANSIÇÃO AMAZÔNIA-CERRADO

2019 ◽  
Vol 24 ◽  
Author(s):  
Mayara Lucyanne Santos de Araújo ◽  
Jessflan Rafael Nascimento Santos ◽  
Francisco Emenson Carpegiane Silva Feitosa ◽  
Juliana Sales dos Santos ◽  
Vilena Aparecida Ribeiro Silva ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Global Land ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Assimilation System

Os processos radiativos na superfície são de crucial importância para redistribuição de umidade e de calor no solo e na atmosfera, pois as trocas de calor e umidade afetam o comportamento da biosfera, do tempo e do clima na Terra. Os estudos de modelagem climática utilizam como parâmetros os componentes do balanço de radiação e calor na superfície, com as trocas de energia na interface vegetação-atmosfera. O objetivo do presente trabalho foi caracterizar espaço-temporalmente os componentes do balanço de radiação e calor no Maranhão, por meio de dados provenientes da base Global Land Data Assimilation System – GLDAS, no período de 2000 a 2014. Foram realizadas técnicas de estatística descritiva, como a medida de tendência central e de variabilidade de dados para caracterização dos mesmos. As médias mensais da radiação de ondas curtas incidente e do saldo de radiação de ondas curtas, no bioma Amazônico, não evidenciaram um comportamento diferenciado entre os períodos seco e chuvoso. Por sua vez, a radiação de ondas curtas incidente e o saldo de radiação de ondas curtas, no bioma Cerrado, apresentaram médias mensais superiores no período seco. O fluxo de calor sensível e o fluxo de calor no solo, nos biomas Cerrado e Amazônia, também apresentaram médias mensais superiores no período seco. A radiação de ondas longas incidente, o saldo de radiação de ondas longas e o fluxo de calor latente, nos biomas Cerrado e Amazônia, apresentaram médias mensais superiores no período chuvoso. De maneira geral, a base de dados GLDAS viabiliza os estudos de planejamento ambiental em regiões de escassez de dados climáticos, como o estado do Maranhão.

scholarly journals Analysis of Multiple Precipitation Products and Preliminary Assessment of Their Impact on Global Land Data Assimilation System Land Surface States

2005 ◽  
Vol 6 (5) ◽  
pp. 573-598 ◽  
Author(s):  
Jon Gottschalck ◽  
Jesse Meng ◽  
Matt Rodell ◽  
Paul Houser
Keyword(s):  
United States ◽  
Data Assimilation ◽  
Land Surface ◽  
Surface States ◽  
Rain Gauge ◽  
Global Land ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Assimilation System

Abstract Precipitation is arguably the most important meteorological forcing variable in land surface modeling. Many types of precipitation datasets exist (with various pros and cons) and include those from atmospheric data assimilation systems, satellites, rain gauges, ground radar, and merged products. These datasets are being evaluated in order to choose the most suitable precipitation forcing for real-time and retrospective simulations of the Global Land Data Assimilation System (GLDAS). This paper first presents results of a comparison for the period from March 2002 to February 2003. Later, GLDAS simulations 14 months in duration are analyzed to diagnose impacts on GLDAS land surface states when using the Mosaic land surface model (LSM). A comparison of seasonal total precipitation for the continental United States (CONUS) illustrates that the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) has the closest agreement with a CPC rain gauge dataset for all seasons except winter. The European Centre for Medium-Range Weather Forecasts (ECMWF) model performs the best of the modeling systems. The satellite-only products [the Tropical Rainfall Measuring Mission (TRMM) Real-time Multi-satellite Precipitation Analysis and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)] suffer from a few deficiencies—most notably an overestimation of summertime precipitation in the central United States (200–400 mm). CMAP is the most closely correlated with daily rain gauge data for the spring, fall, and winter seasons, while the satellite-only estimates perform best in summer. GLDAS land surface states are sensitive to different precipitation forcing where percent differences in volumetric soil water content (SWC) between simulations ranged from −75% to +100%. The percent differences in SWC are generally 25%–75% less than the percent precipitation differences, indicating that GLDAS and specifically the Mosaic LSM act to generally “damp” precipitation differences. Areas where the percent changes are equivalent to the percent precipitation changes, however, are evident. Soil temperature spread between GLDAS runs was considerable and ranged up to ±3.0 K with the largest impact in the western United States.

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