Wind Speed and Altitude Dependent AMDAR Observational Error and Its Impacts on Data Assimilation and Forecasting

2020 ◽  
Vol 26 (3) ◽  
pp. 261-274
Author(s):  
CHEN Yao-deng ◽  
ZHOU Bing-jun ◽  
CHEN Min ◽  
WANG Yuan-bing
Keyword(s):  
Wind Speed ◽  
Data Assimilation ◽  
Observational Error

scholarly journals Assimilation of Satellite-Derived Soil Moisture for Improved Forecasts of the Great Plains Low-Level Jet

2020 ◽  
Vol 148 (11) ◽  
pp. 4607-4627
Author(s):  
Craig R. Ferguson ◽  
Shubhi Agrawal ◽  
Mark C. Beauharnois ◽  
Geng Xia ◽  
D. Alex Burrows ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Wind Speed ◽  
Data Assimilation ◽  
Great Plains ◽  
Wrf Model ◽  
Added Value ◽  
Low Level ◽  
Convective Systems ◽  
South Central ◽  
Mesoscale Convective

AbstractIn the context of forecasting societally impactful Great Plains low-level jets (GPLLJs), the potential added value of satellite soil moisture (SM) data assimilation (DA) is high. GPLLJs are both sensitive to regional soil moisture gradients and frequent drivers of severe weather, including mesoscale convective systems. An untested hypothesis is that SM DA is more effective in forecasts of weakly synoptically forced, or uncoupled GPLLJs, than in forecasts of cyclone-induced coupled GPLLJs. Using the NASA Unified Weather Research and Forecasting (NU-WRF) Model, 75 GPLLJs are simulated at 9-km resolution both with and without NASA Soil Moisture Active Passive SM DA. Differences in modeled SM, surface sensible (SH) and latent heat (LH) fluxes, 2-m temperature (T2), 2-m humidity (Q2), PBL height (PBLH), and 850-hPa wind speed (W850) are quantified for individual jets and jet-type event subsets over the south-central Great Plains, as well as separately for each GPLLJ sector (entrance, core, and exit). At the GPLLJ core, DA-related changes of up to 5.4 kg m−2 in SM can result in T2, Q2, LH, SH, PBLH, and W850 differences of 0.68°C, 0.71 g kg−2, 59.9 W m−2, 52.4 W m−2, 240 m, and 4 m s−1, respectively. W850 differences focus along the jet axis and tend to increase from south to north. Jet-type differences are most evident at the GPLLJ exit where DA increases and decreases W850 in uncoupled and coupled GPLLJs, respectively. Data assimilation marginally reduces negative wind speed bias for all jets, but the correction is greater for uncoupled GPLLJs, as hypothesized.

Ensemble-Based Data Assimilation for Wind Forecasting – Application to Wind Farm

2020 ◽  
Author(s):  
Yang-Ming Fan
Keyword(s):  
Wind Speed ◽  
Natural Gas ◽  
Data Assimilation ◽  
Wind Energy ◽  
Wind Farm ◽  
Initial Conditions ◽  
Wind Farms ◽  
Weather Conditions ◽  
Energy Output ◽  
Combining Data

<p>The purpose of this study is to develop an ensemble-based data assimilation method to accurately predict wind speed in wind farm and provide it for the use of wind energy intelligent forecasting platform. As Taiwan government aimed to increase the share of renewable energy generation to 20% by 2025, among them, the uncertain wind energy output will cause electricity company has to reserve a considerable reserve capacity when dispatching power, and it is usually high cost natural gas power generation. In view of this, we will develop wind energy intelligent forecasting platform with an error of 10% within 72 hours and expect to save hundred millions of dollars of unnecessary natural gas generators investment. Once the wind energy can be predicted more accurately, the electricity company can fully utilize the robustness and economy of smart grid supply. Therefore, the mastery of the change of wind speed is one of the key factors that can reduce the minimum error of wind energy intelligent forecasting.</p><p>There are many uncertainties in the numerical meteorological models, including errors in the initial conditions or defects in the model, which may affect the accuracy of the prediction. Since the deterministic prediction cannot fully grasp the uncertainty in the prediction process, so it is difficult to obtain all possible wind field changes. The development of ensemble-based data assimilation prediction is to make up for the weakness of deterministic prediction. With the prediction of 20 wind fields as ensemble members, it is expected to include the uncertainty of prediction, quantify the uncertainty, and integrate the wind speed observations of wind farms as well to provide the optimal prediction of wind speed for the next 72 hours. The results show that the prediction error of wind speed within 72 hours is 6% under different weather conditions (excluding typhoons), which proves that the accuracy of wind speed prediction by combining data assimilation technology and ensemble approach is better.</p>

Assimilation of Satellite-retrieved Sea Ice Concentration and Prospects for September Predictions of Arctic Sea Ice

2020 ◽  
pp. 1-49
Author(s):  
Yong-Fei Zhang ◽  
Mitchell Bushuk ◽  
Michael Winton ◽  
Bill Hurlin ◽  
Xiaosong Yang ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Sea Surface ◽  
Prediction System ◽  
Observational Error ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Ice Concentration ◽  
Analysis Errors

AbstractThe current GFDL seasonal prediction system achieved retrospective sea ice extent (SIE) skill without direct sea ice data assimilation. Here we develop sea ice data assimilation, shown to be a key source of skill for seasonal sea ice predictions, in GFDL’s next generation prediction system, the Seamless System for Prediction and Earth System Research (SPEAR). Satellite sea-ice concentration (SIC) observations are assimilated into the GFDL Sea Ice Simulator version 2 (SIS2) using the ensemble adjustment Kalman filter (EAKF). Sea ice physics is perturbed to form an ensemble of ice-ocean members with atmospheric forcing from the JRA-55 reanalysis. Assimilation is performed every 5 days from 1982 to 2017 and the evaluation is conducted at pan-Arctic and regional scales over the same period. To mitigate an assimilation overshoot problem and improve the analysis, sea surface temperatures (SST) are restored to the daily Optimum Interpolation Sea Surface Temperature version 2 (OISSTv2). The combination of SIC assimilation and SST restoring reduces analysis errors to the observational error level (∼10%) from up to 3 times larger than this (∼30%) in the free-running model. Sensitivity experiments show that the choice of assimilation localization half-width (190km) is near optimal and that SIC analysis errors can be further reduced slightly either by reducing the observational error or by increasing the assimilation frequency from 5-daily to daily. A lagged-correlation analysis suggests substantial prediction skill improvements from SIC initialization at lead times of less than 2 months.

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 Numerical Simulation of Near-Surface Wind during a Severe Wind Event in a Complex Terrain by Multisource Data Assimilation and Dynamic Downscaling

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
De Zhang ◽  
Luyuan Chen ◽  
Feimin Zhang ◽  
Juan Tan ◽  
Chenghai Wang
Keyword(s):  
Wind Speed ◽  
Data Assimilation ◽  
Surface Wind ◽  
Wrf Model ◽  
Surface Wind Speed ◽  
Dynamic Downscaling ◽  
Near Surface ◽  
Wind Event ◽  
Weak Wind

Accurate forecast and simulation of near-surface wind is a great challenge for numerical weather prediction models due to the significant transient and intermittent nature of near-surface wind. Based on the analyses of the impact of assimilating in situ and Advanced Tiros Operational Vertical Sounder (ATOVS) satellite radiance data on the simulation of near-surface wind during a severe wind event, using the new generation mesoscale Weather Research and Forecasting (WRF) model and its three-dimensional variational (3DVAR) data assimilation system, the dynamic downscaling of near-surface wind is further investigated by coupling the microscale California Meteorological (CALMET) model with the WRF and its 3DVAR system. Results indicate that assimilating in situ and ATOVS radiance observations strengthens the airflow across the Alataw valley and triggers the downward transport of momentum from the upper atmosphere in the downstream area of the valley in the initial conditions, thus improving near-surface wind simulations. Further investigations indicate that the CALMET model provides more refined microtopographic structures than the WRF model in the vicinity of the wind towers. Although using the CALMET model achieves the best simulation of near-surface wind through dynamic downscaling of the output from the WRF and its 3DVAR assimilation, the simulation improvements of near-surface wind speed are mainly within 1 m s−1. Specifically, the mean improvement proportions of near-surface wind speed are 64.8% for the whole simulation period, 58.7% for the severe wind period, 68.3% for the severe wind decay period, and 75.4% for the weak wind period. The observed near-surface wind directions in the weak wind conditions are better simulated in the coupled model with CALMET downscaling than in the WRF and its 3DVAR system. It is concluded that the simulation improvements of CALMET downscaling are distinct when near-surface winds are weak, and the downscaling effects are mainly manifested in the simulation of near-surface wind directions.

scholarly journals Wind Speed Retrieval Based on Sea Surface Roughness Measurements from Spaceborne Microwave Radiometers

2013 ◽  
Vol 52 (2) ◽  
pp. 507-516 ◽  
Author(s):  
Sungwook Hong ◽  
Inchul Shin
Keyword(s):  
Surface Roughness ◽  
Wind Speed ◽  
Data Assimilation ◽  
Thermal Emission ◽  
Surface Wind ◽  
Sea Surface ◽  
Retrieval Algorithm ◽  
Surface Emissivity ◽  
Wide Range ◽  
Global Data

AbstractWind speed is the main factor responsible for the increase in ocean thermal emission because sea surface emissivity strongly depends on surface roughness. An alternative approach to estimate the surface wind speed (SWS) as a function of surface roughness is developed in this study. For the sea surface emissivity, the state-of-the-art forward Fast Microwave Emissivity Model, version 3 (FASTEM-3), which is applicable for a wide range of microwave frequencies at incidence angles of less than 60°, is used. Special Sensor Microwave Imager and Advanced Microwave Scanning Radiometer (AMSR-E) observations are simulated using FASTEM-3 and the Global Data Assimilation and Prediction System operated by the Korea Meteorological Administration. The performance of the SWS retrieval algorithm is assessed by comparing its SWS output to that of the Global Data Assimilation System operated by the National Centers for Environmental Prediction. The surface roughness is computed using the Hong approximation and characteristics of the polarization ratio. When compared with the Tropical Atmosphere–Ocean data, the bias and root-mean-square error (RMSE) of the SWS outputs from the proposed wind speed retrieval algorithm were found to be 0.32 m s−1 (bias) and 0.37 m s−1 (RMSE) for the AMSR-E 18.7-GHz channel, 0.38 m s−1 (bias) and 0.42 m s−1 (RMSE) for the AMSR-E 23.8-GHz channel, and 0.45 m s−1 (bias) and 0.49 m s−1 (RMSE) for the AMSR-E 36.5-GHz channel. Consequently, this research provides an alternative method to retrieve the SWS with minimal a priori information on the sea surface.

Hierarchical Cluster Analysis of a Convection-Allowing Ensemble during the Hazardous Weather Testbed 2009 Spring Experiment. Part II: Ensemble Clustering over the Whole Experiment Period

2011 ◽  
Vol 139 (12) ◽  
pp. 3694-3710 ◽  
Author(s):  
Aaron Johnson ◽  
Xuguang Wang ◽  
Ming Xue ◽  
Fanyou Kong
Keyword(s):  
Cluster Analysis ◽  
Wind Speed ◽  
Data Assimilation ◽  
Hierarchical Cluster Analysis ◽  
Initial Conditions ◽  
Hierarchical Cluster ◽  
Lead Times ◽  
Ensemble Forecasts ◽  
Pbl Scheme ◽  
Hazardous Weather

Abstract Twenty-member real-time convection-allowing storm-scale ensemble forecasts with perturbations to model physics, dynamics, initial conditions (IC), and lateral boundary conditions (LBC) during the NOAA Hazardous Weather Testbed Spring Experiment provide a unique opportunity to study the relative impact of different sources of perturbation on convection-allowing ensemble diversity. In Part II of this two-part study, systematic similarity/dissimilarity of hourly precipitation forecasts among ensemble members from the spring season of 2009 are identified using hierarchical cluster analysis (HCA) with a fuzzy object-based threat score (OTS), developed in Part I. In addition to precipitation, HCA is also performed on ensemble forecasts using the traditional Euclidean distance for wind speed at 10 m and 850 hPa, and temperature at 500 hPa. At early lead times (3 h, valid at 0300 UTC) precipitation forecasts cluster primarily by data assimilation and model dynamic core, indicating a dominating impact of models, with secondary clustering by microphysics. There is an increasing impact of the planetary boundary layer (PBL) scheme on clustering relative to the microphysics scheme at later lead times. Forecasts of 10-m wind speed cluster primarily by the PBL scheme at early lead times, with an increasing impact of LBC at later lead times. Forecasts of midtropospheric variables cluster primarily by IC at early lead times and LBC at later lead times. The radar and Mesonet data assimilation (DA) show its impact, with members without DA in a distinct cluster, through the 12-h lead time (valid at 1200 UTC) for both precipitation and nonprecipitation variables. The implication for optimal ensemble design for storm-scale forecasts is also discussed.

scholarly journals Wind Energy Potential Assessment Based-on WRF Four-Dimensional Data Assimilation System and Cross-Calibrated Multi-Platform Dataset

2021 ◽  
Vol 897 (1) ◽  
pp. 012004
Author(s):  
Nurry Widya Hesty ◽  
Dian Galuh Cendrawati ◽  
Rabindra Nepal ◽  
Muhammad Indra Al Irsyad
Keyword(s):  
Renewable Energy ◽  
Wind Speed ◽  
Data Assimilation ◽  
Wind Energy ◽  
Wrf Model ◽  
Energy Potential ◽  
High Resolution Data ◽  
Energy Share ◽  
Environmental Prediction ◽  
Four Dimensional Data Assimilation

Abstract Indonesia has a target of achieving 23% of renewable energy share in the total energy mix in 2025. However, Indonesia does not have accurate and comprehensive data on renewable energy potentials, especially wind energy. This article aims to assess the theoretical potential of wind speed and to visualize the wind speed by province for the entire Indonesia. Our assessment relied on the Weather Research and Forecasting (WRF) model using Four-Dimensional Data Assimilation technique, also known as Nudging Newtonian relaxation. The robustness of our analysis is confirmed by using high-resolution data from the National Centers for Environmental Prediction–Final (NCEP - FNL) and Cross-Calibrated Multi-Platform (CCMP) Reanalysis satellite data. This study shows the WRF method is a feasible option to estimate wind speed data.

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