scholarly journals Use of the WRF-DA 3D-Var Data Assimilation System to Obtain Wind Speed Estimates in Regular Grids from Measurements at Wind Farms in Uruguay

Data ◽  
2019 ◽  
Vol 4 (4) ◽  
pp. 142 ◽  
Author(s):  
Gabriel Cazes Boezio ◽  
Sofía Ortelli
Keyword(s):  
Wind Speed ◽  
Data Assimilation ◽  
Wind Farms ◽  
Forecast Model ◽  
Model Data ◽  
Assimilation Process ◽  
The Mean ◽  
Data Assimilation System ◽  
Assimilation System

This work assessed the quality of wind speed estimates in Uruguay. These estimates were obtained using the Weather Research and Forecast Model Data Assimilation System (WRF-DA) to assimilate wind speed measurements from 100 m above the ground at two wind farms. The quality of the estimates was assessed with an anemometric station placed between the wind farms. The wind speed estimates showed low systematic errors at heights of 87 and 36 m above the ground. At both levels, the standard deviation of the total errors was approximately 25% of the mean observed speed. These results suggested that the estimates obtained could be of sufficient quality to be useful in various applications. The assimilation process proved to be effective, spreading the observational gain obtained at the wind farms to lower elevations than those at which the assimilated measurements were taken. The smooth topography of Uruguay might have contributed to the relatively good quality of the obtained wind estimates, although the data of only two stations were assimilated, and the resolution of the regional atmospheric simulations employed was relatively low.

scholarly journals Impact of Multiple Altimeter Data and Mean Dynamic Topography in a Global Analysis and Forecasting System

2019 ◽  
Vol 36 (7) ◽  
pp. 1255-1266 ◽  
Author(s):  
Mathieu Hamon ◽  
Eric Greiner ◽  
Pierre-Yves Le Traon ◽  
Elisabeth Remy
Keyword(s):  
Data Assimilation ◽  
Sea Surface ◽  
Global Ocean ◽  
Altimeter Data ◽  
Dynamic Topography ◽  
Mean Dynamic Topography ◽  
Ocean Data Assimilation ◽  
The Mean ◽  
Data Assimilation System ◽  
Assimilation System

AbstractSatellite altimetry is one of the main sources of information used to constrain global ocean analysis and forecasting systems. In addition to in situ vertical temperature and salinity profiles and sea surface temperature (SST) data, sea level anomalies (SLA) from multiple altimeters are assimilated through the knowledge of a surface reference, the mean dynamic topography (MDT). The quality of analyses and forecasts mainly depends on the availability of SLA observations and on the accuracy of the MDT. A series of observing system evaluations (OSEs) were conducted to assess the relative importance of the number of assimilated altimeters and the accuracy of the MDT in a Mercator Ocean global 1/4° ocean data assimilation system. Dedicated tools were used to quantify impacts on analyzed and forecast sea surface height and temperature/salinity in deeper layers. The study shows that a constellation of four altimeters associated with a precise MDT is required to adequately describe and predict upper-ocean circulation in a global 1/4° ocean data assimilation system. Compared to a one-altimeter configuration, a four-altimeter configuration reduces the mean forecast error by about 30%, but the reduction can reach more than 80% in western boundary current (WBC) regions. The use of the most recent MDT updates improves the accuracy of analyses and forecasts to the same extent as assimilating a fourth altimeter.

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.

scholarly journals An update on the 4D-LETKF data assimilation system for the whole neutral atmosphere

2021 ◽  
Author(s):  
Dai Koshin ◽  
Kaoru Sato ◽  
Masashi Kohma ◽  
Shingo Watanabe
Keyword(s):  
Data Assimilation ◽  
Lower Thermosphere ◽  
Mean Field ◽  
Forecast Model ◽  
Small Scale ◽  
Neutral Atmosphere ◽  
Broadband Emission ◽  
Short Period ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. The four-dimensional local ensemble transform Kalman filter (4D-LETKF) data assimilation system for the whole neutral atmosphere is updated to better represent disturbances with wave periods shorter than 1 day in the mesosphere and lower thermosphere (MLT) region. First, incremental analysis update (IAU) filtering is introduced to reduce the generation of spurious waves arising from the insertion of the analysis updates. The IAU is better than other filtering methods, and also is commonly used for the middle atmospheric data assimilation. Second, the horizontal diffusion in the forecast model is modified to reproduce the more realistic tidal amplitudes that were observed by satellites. Third, the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) and Special Sensor Microwave Imager/Sounder (SSMIS) observations in the stratosphere and mesosphere also are assimilated. The performance of the resultant analyses is evaluated by comparing them with the mesospheric winds from meteor radars, which are not assimilated. The representation of assimilation products is greatly improved not only for the zonal mean field but also for short-period and/or horizontally small-scale disturbances.

scholarly journals Effects of model chemistry and data biases on stratospheric ozone assimilation

2007 ◽  
Vol 7 (11) ◽  
pp. 2917-2935 ◽  
Author(s):  
L. Coy ◽  
D. R. Allen ◽  
S. D. Eckermann ◽  
J. P. McCormack ◽  
I. Stajner ◽  
...  
Keyword(s):  
Stratospheric Ozone ◽  
Region Of Interest ◽  
Forecast Model ◽  
Testing System ◽  
The Mean ◽  
Ozone Photochemistry ◽  
Data Assimilation System ◽  
Upper Boundary ◽  
F Statistics

Abstract. The innovations or observation minus forecast (O–F) residuals produced by a data assimilation system provide a convenient metric of evaluating global analyses. In this study, O–F statistics from the Global Ozone Assimilation Testing System (GOATS) are used to examine how ozone assimilation products and their associated O–F statistics depend on input data biases and ozone photochemistry parameterizations (OPP). All the GOATS results shown are based on a 6-h forecast and analysis cycle using observations from SBUV/2 (Solar Backscatter UltraViolet instrument-2) during September–October 2002. Results show that zonal mean ozone analyses are more independent of observation biases and drifts when using an OPP, while the mean ozone O–Fs are more sensitive to observation drifts when using an OPP. In addition, SD O–Fs (standard deviations) are reduced in the upper stratosphere when using an OPP due to a reduction of forecast model noise and to increased covariance between the forecast model and the observations. Experiments that changed the OPP reference state to match the observations by using an "adaptive" OPP scheme reduced the mean ozone O–Fs at the expense of zonal mean ozone analyses being more susceptible to data biases and drifts. Additional experiments showed that the upper boundary of the ozone DAS can affect the quality of the ozone analysis and therefore should be placed well above (at least a scale height) the region of interest.

scholarly journals Data assimilation of dust aerosol observations for the CUACE/dust forecasting system

2008 ◽  
Vol 8 (13) ◽  
pp. 3473-3482 ◽  
Author(s):  
T. Niu ◽  
S. L. Gong ◽  
G. F. Zhu ◽  
H. L. Liu ◽  
X. Q. Hu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Atmospheric Chemistry ◽  
Three Dimensional ◽  
Forecast Model ◽  
System A ◽  
Operational Forecasts ◽  
Forecasting System ◽  
Dust Loading ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. A data assimilation system (DAS) was developed for the Chinese Unified Atmospheric Chemistry Environment – Dust (CUACE/Dust) forecast system and applied in the operational forecasts of sand and dust storm (SDS) in spring 2006. The system is based on a three dimensional variational method (3D-Var) and uses extensively the measurements of surface visibility (phenomena) and dust loading retrieval from the Chinese geostationary satellite FY-2C. By a number of case studies, the DAS was found to provide corrections to both under- and over-estimates of SDS, presenting a major improvement to the forecasting capability of CUACE/Dust in the short-term variability in the spatial distribution and intensity of dust concentrations in both source regions and downwind areas. The seasonal mean Threat Score (TS) over the East Asia in spring 2006 increased from 0.22 to 0.31 by using the data assimilation system, a 41% enhancement. The forecast results with DAS usually agree with the dust loading retrieved from FY-2C and visibility distribution from surface meteorological stations, which indicates that the 3D-Var method is very powerful by the unification of observation and numerical model to improve the performance of forecast model.

scholarly journals A novel model–data fusion approach to terrestrial carbon cycle reanalysis across the contiguous U.S using SIPNET and PEcAn state data assimilation system v. 1.7.2

2021 ◽  
Author(s):  
Hamze Dokoohaki ◽  
Bailey D. Morrison ◽  
Ann Raiho ◽  
Shawn P. Serbin ◽  
Michael Dietze
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Soil Carbon ◽  
Model Data ◽  
Terrestrial Carbon ◽  
State Data ◽  
Flux Tower ◽  
Terrestrial Carbon Cycle ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. The ability to monitor, understand, and predict the dynamics of the terrestrial carbon cycle requires the capacity to robustly and coherently synthesize multiple streams of information that each provide partial information about different pools and fluxes. In this study, we introduce a new terrestrial carbon cycle data assimilation system, built on the PEcAn model-data eco-informatics system, and its application for the development of a proof-of-concept carbon "reanalysis" product that harmonizes carbon pools (leaf, wood, soil) and fluxes (GPP, Ra, Rh, NEE) across the contiguous United States from 1986–2019. We first calibrated this system against plant trait and flux tower Net Ecosystem Exchange (NEE) using a novel emulated hierarchical Bayesian approach. Next, we extended the Tobit-Wishart Ensemble Filter (TWEnF) State Data Assimilation (SDA) framework, a generalization of the common Ensemble Kalman Filter which accounts for censored data and provides a fully Bayesian estimate of model process error, to a regional-scale system with a calibrated localization. Combined with additional workflows for propagating parameter, initial condition, and driver uncertainty, this represents the most complete and robust uncertainty accounting available for terrestrial carbon models. Our initial reanalysis was run on an irregular grid of ~500 points selected using a stratified sampling method to efficiently capture environmental heterogeneity. Remotely sensed observations of aboveground biomass (Landsat LandTrendr) and LAI (MODIS MOD15) were sequentially assimilated into the SIPNET model. Reanalysis soil carbon, which was indirectly constrained based on modeled covariances, showed general agreement with SoilGrids, an independent soil carbon data product. Reanalysis NEE, which was constrained based on posterior ensemble weights, also showed good agreement with eddy flux tower NEE and reduced RMSE compared to the calibrated forecast. Ultimately, PEcAn's carbon cycle reanalysis provides a scalable framework for harmonizing multiple data constraints and providing a uniform synthetic platform for carbon monitoring, reporting, and verification (MRV) and accelerating terrestrial carbon cycle research.

scholarly journals A simulation study of the ensemble-based data assimilation of satellite-borne lidar aerosol observations

2012 ◽  
Vol 5 (3) ◽  
pp. 1877-1947 ◽  
Author(s):  
T. T. Sekiyama ◽  
T. Y. Tanaka ◽  
T. Miyoshi
Keyword(s):  
Data Assimilation ◽  
Dust Emission ◽  
Lidar Data ◽  
Sulfate Aerosols ◽  
Dust Aerosols ◽  
Collection Period ◽  
The Mean ◽  
Meteorological Observations ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. A four-dimensional ensemble-based data assimilation system was assessed by observing system simulation experiments (OSSEs), in which the CALIPSO satellite was emulated via simulated satellite-borne lidar aerosol observations. Its performance over athree-month period was validated according to the Method for Object-based Diagnostic Evaluation (MODE), using aerosol optical thickness (AOT) distributions in East Asia as the objects of analysis. Consequently, this data assimilation system demonstrated the ability to produce better analyses of sulfate and dust aerosols in comparison to a free-running simulation model. For example, the mean centroid distance (from the truth) over a three-month collection period of aerosol plumes was improved from 2.15 grids (≈ 600 km) to 1.45 grids (≈ 400 km) for sulfate aerosols and from 2.59 grids (≈ 750 km) to 1.14 grids (≈ 330 km) for dust aerosols; the mean area ratio (to the truth) over a three-month collection period of aerosol plumes was improved from 0.49 to 0.76 for sulfate aerosols and from 0.51 to 0.72 for dust aerosols. The satellite-borne lidar data assimilation successfully improved the aerosol plume analysis and the dust emission estimation in the OSSEs. These results present great possibilities for the beneficial use of lidar data, whose distribution is vertically/temporally dense but horizontally sparse, when coupled with a four-dimensional data assimilation system. In addition, sensitivity tests were conducted, and their results indicated that the degree of freedom to control the aerosol variables was probably limited in the data assimilation because the meteorological field in the system was constrained to weather reanalysis using Newtonian relaxation. Further improvements to the aerosol analysis can be performed through the simultaneous assimilation of aerosol observations with meteorological observations. The OSSE results strongly suggest that the use of real CALIPSO data will have a beneficial effect on obtaining more accurate sulfate and dust aerosol analyses. Furthermore, the use of the same OSSE technique will allow us to perform a prior assessment of the next-generation lidar satellite EarthCARE, which will be launched in 2015.

Features of formation and evolution of a polar cyclone in the Barents and Kara seas in December 2020

2021 ◽  
Vol 3 ◽  
pp. 44-52
Author(s):  
E.S. Nesterov ◽  
◽  
V.D. Zhupanov ◽  
A.V. Fedorenko ◽  
◽  
...  
Keyword(s):  
Wind Speed ◽  
Data Assimilation ◽  
Kara Sea ◽  
Heat Fluxes ◽  
Sensible Heat ◽  
Global Data Assimilation System ◽  
Formation And Evolution ◽  
Data Assimilation System ◽  
Global Data ◽  
Assimilation System

Based on the GDAS (Global Data Assimilation System) information, the conditions for the formation and evolution of a polar cyclone in the Barents Sea in December 2020 are studied. The cyclone in interesting because after entering the Kara Sea, the cyclone did not weaken as usual but continued to intensify. The formation of the cyclone occurred near the ice edge under conditions of the cold Arctic air outbreak. After entering the Kara Sea, the cyclone continued to deepen, which was facilitated by latent and sensible heat fluxes in the ice-free western part of the sea. According to weather station data, when the cyclone moved further over land, the wind direction changed from mainly southern to northern, and wind speed increased significantly. There also was a sharp decrease in air temperature by 7–10 °С for three hours. Keywords: polar cyclone, global data assimilation system, wind speed, latent and sensible heat fluxes

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