Real-Time Four-Dimensional Data Assimilation Research Articles

Wind Speed Forecasts of a Mesoscale Ensemble for Large-Scale Wind Farms in Northern China: Downscaling Effect of Global Model Forecasts

To facilitate wind power integration for the electric power grid operated by the Inner Mongolia Electric Power Corporation—a major electric power grid in China—a high-resolution (of 2.7 km grid intervals) mesoscale ensemble prediction system was developed that forecasts winds for 130 wind farms in the Inner Mongolia Autonomous Region. The ensemble system contains 39 forecasting members that are divided into 3 groups; each group is composed of the NCAR (National Center for Atmospheric Research) real-time four-dimensional data assimilation and forecasting model (RTFDDA) with 13 physical perturbation members, but driven by the forecasts of the GFS (Global Forecast System), GEM (Global Environmental Multiscale Model), and GEOS (Goddard Earth Observing System), respectively. The hub-height wind predictions of these three sub-ensemble groups at selected wind turbines across the region were verified against the hub-height wind measurements. The forecast performance and variations with lead time, wind regimes, and diurnal and regional changes were analyzed. The results show that the GFS group outperformed the other two groups with respect to correlation coefficient and mean absolute error. The GFS group had the most accurate forecasts in ~59% of sites, while the GEOS and GEM groups only performed the best on 34% and 2% of occasions, respectively. The wind forecasts were most accurate for wind speeds ranging from 3 to 12 m/s, but with an overestimation for low speeds and an underestimation for high speeds. The GEOS-driven members obtained the least bias error among the three groups. All members performed rather accurately in daytime, but evidently overestimated the winds during nighttime. The GFS group possessed the fewest diurnal errors, and the bias of the GEM group grew significantly during nighttime. The wind speed forecast errors of all three ensemble members increased with the forecast lead time, with the average absolute error increasing by ~0.3 m/s per day during the first 72 h of forecasts.

Assimilation of GNSS PWV with NCAR-RTFDDA to Improve Prediction of a Landfall Typhoon

Precipitable water vapor (PWV) retrieved from ground-based global navigation satellite system (GNSS) stations acquisition signal of a navigation satellite system provides high spatial and temporal resolution atmospheric water vapor. In this paper, an observation-nudging-based real-time four-dimensional data assimilation (RTFDDA) approach was used to assimilate the PWV estimated from GNSS observation into the WRF (Weather Research and Forecasting) modeling system. A landfall typhoon, “Mangkhut”, is chosen to evaluate the impact of GNSS PWV data assimilation on its track, intensity, and precipitation prediction. The results show that RTFDDA can assimilate GNSS PWV data into WRF to improve the water vapor distribution associated with the typhoon. Assimilating the GNSS PWV improved the typhoon track and intensity prediction when and after the typhoon made landfall, correcting a 5–10 hPa overestimation (too deep) of the central pressure of the typhoon at landfall. It also improved the occurrence and the intensity of the major typhoon spiral rainbands.

Seasonal variation of the surface wind forecast performance of the high-resolution WRF-RTFDDA system over China

Wind forecast is an essential part of both weather and renewable energy predictions. This study investigated the performances of the weather research and forecasting (WRF) model real-time four-dimensional data assimilation (RTFDDA) and forecasting system for surface wind forecast over China. The system has been running operationally since 2016 with an analysis/forecast cycle every 3 h. The surface wind forecasting skill of the system was evaluated on the 3 km output with the evaluation period of one year from June 2017 to May 2018. The model outputs were validated against observations from the stations over China objectively. The statistics of the system performance was calculated for both station-by-station and the domain average, which include bias, root mean square errors, mean absolute errors, and the correlation between observation and model outputs. The error statistics show that the high-resolution model has advantages in forecasting the detailed structures of weather features and adding values with rapidly refreshing forecast cycles. The verification results demonstrate that WRF tends to forecast surface winds with positive bias for weak wind regimes and negative for high wind regimes. On the other hand, it tends to produce positive bias for lower topography regions and negative for high topography areas. This feature does not change with seasons although the magnitude of wind bias varies with different seasons. The wind forecast bias has the largest diurnal changes in summer, with positive bias close to the coastal areas and the downstream of Yangtze river.

Forecasting severe convective storms with WRF-based RTFDDA radar data assimilation in Guangdong, China

Radar data assimilation is an important method for short-term convection forecasting or nowcasting. To improve the short-term (mainly 0–3 h) precipitation forecasts for severe convective storms, an analysis nudging (Newtonian relaxation) based hydrometeor and latent heat nudging (HLHN) technique was developed to effectively assimilate radar reflectivity data in a Weather Research and Forecasting (WRF)-based real time four-dimensional data assimilation and short-term forecasting system (RTFDDA). The purpose of this study is to investigate the performance of the RTFDDA system with radar data assimilation (RTFDDA-RDA) with rapid-cycling forecasting applications for Shenzhen, a subtropical coastal metropolis in southern China. The RTFDDA-RDA system was run to produce hindcasts for ten severe convective storm events occurred in Guangdong region during the 2017 rainy season. Results show that, through nudging cloud hydrometeors retrieved from radar reflectivity and the associated latent heat release, RTFDDA-RDA is able to produce the meso- and convective-scale features of the convective storms in a good accuracy and improve the short-term precipitation forecasting of the convective storms. Subjective and statistical evaluation results demonstrate that RTFDDA-RDA presents a reasonable capability for forecasting convective systems with improving the initial conditions and resulting in significant improvements of precipitation forecasting skills, especially for the 0–3-h nowcasting range. The sensitivity experiments on different latent heating schemes show that, the convective-stratiform separated heating scheme has the best performance of forecasts. Finally, intercomparison of different radar data assimilation approaches will be conducted in future.

Incorporating geostationary lightning data into a radar reflectivity based hydrometeor retrieval method: An observing system simulation experiment

A retrieval method for deriving the hydrometeor mixing ratio within mesoscale convective system (MCS) is presented in this study. The hydrometeor retrieval method was designed to incorporate the flash extent densities (FED) data from the Feng-Yun-4 geostationary satellite into the S-band radar reflectivity (Zh) and ambient temperature (T) data-based hydrometeor retrieval method. Total lightning data are utilized to better discern regions containing graupel in clouds. In the quantitative estimation of rain mixing ratio, different intercept parameters are used for different ranges of Zh and different estimated precursors of raindrop in cold-cloud microphysical processes (i.e., graupel and snow aggregate). The hydrometeor retrieval method was evaluated through an observing system simulation experiment (OSSE) in which the pseudo-input-data for the hydrometeor retrieval (i.e., the FED, Zh and T data) were obtained from the cloud-scale (1-km) simulation of an MCS using explicit electrification implemented within the Weather Research and Forecasting model. By incorporating the FED data as an additional input data source into the Zh and T-based hydrometeor retrieval method, the hydrometeor retrieval accuracy was improved. The hydrometeor retrievals were then assimilated into the model using the Real-Time Four-Dimensional Data Assimilation (RTFDDA) system. Assimilating more accurate hydrometeor fields slightly improved the analyses and forecasts of convective precipitation in the test MCS case. The improvement could be due to the more accurate hydrometeor analysis, which further affected the strength of the cold pool and gust front.

Evaluations of WRF Sensitivities in Surface Simulations with an Ensemble Prediction System

This paper investigates the sensitivities of the Weather Research and Forecasting (WRF) model simulations to different parameterization schemes (atmospheric boundary layer, microphysics, cumulus, longwave and shortwave radiations and other model configuration parameters) on a domain centered over the inter-mountain western United States (U.S.). Sensitivities are evaluated through a multi-model, multi-physics and multi-perturbation operational ensemble system based on the real-time four-dimensional data assimilation (RTFDDA) forecasting scheme, which was developed at the National Center for Atmospheric Research (NCAR) in the United States. The modeling system has three nested domains with horizontal grid intervals of 30 km, 10 km and 3.3 km. Each member of the ensemble system is treated as one of 48 sensitivity experiments. Validation with station observations is done with simulations on a 3.3-km domain from a cold period (January) and a warm period (July). Analyses and forecasts were run every 6 h during one week in each period. Performance metrics, calculated station-by-station and as a grid-wide average, are the bias, root mean square error (RMSE), mean absolute error (MAE), normalized standard deviation and the correlation between the observation and model. Across all members, the 2-m temperature has domain-average biases of −1.5–0.8 K; the 2-m specific humidity has biases from −0.5–−0.05 g/kg; and the 10-m wind speed and wind direction have biases from 0.2–1.18 m/s and −0.5–4 degrees, respectively. Surface temperature is most sensitive to the microphysics and atmospheric boundary layer schemes, which can also produce significant differences in surface wind speed and direction. All examined variables are sensitive to data assimilation.

Evaluation of the Impacts of Assimilating the TAMDAR Data on 12/4 km Grid WRF-Based RTFDDA Simulations over the CONUS

An analysis of the impacts of assimilating the Tropospheric Airborne Meteorological Data Report (TAMDAR) data with the Weather Research and Forecasting- (WRF-) real-time four-dimensional data assimilation (RTFDDA) and forecasting system over the Contiguous US (CONUS) is presented. The impacts of the horizontal resolution increase from 12 km to 4 km on the WRF-RTFDDA simulations are also examined in conjunction with the TAMDAR data impacts. The assimilation of the TAMDAR data reduces the root mean squared error of the moisture field predictions and increases the correlation between the predictions and the observations for both domains with 12 km and 4 km grid spacings. The TAMDAR data reduce the model dry biases in the middle and lower levels by adding moisture at those levels. Assimilating the TAMDAR data improves temperature predictions at middle to high levels and wind speed predictions at all levels especially for the 12 km domain. Increasing the horizontal resolution from 12 km to 4 km results in significantly larger impacts on surface variables than assimilating the TAMDAR data.

Analysis of the surface temperature and wind forecast errors of the NCAR-AirDat operational CONUS 4-km WRF forecasting system

Investigating the characteristics of model-forecast errors using various statistical and object-oriented methods is necessary for providing useful guidance to end-users and model developers as well. To this end, the random and systematic errors (i.e., biases) of the 2-m temperature and 10-m wind predictions of the NCAR-AirDat weather research and forecasting (WRF)-based real-time four-dimensional data assimilation (RTFDDA) and forecasting system are analyzed. This system has been running operationally over a contiguous United States (CONUS) domain at a 4-km grid spacing with four forecast cycles daily from June 2009 to September 2010. In the result an exceptionally useful forecast dataset was generated and used for studying the error properties of the model forecasts, in terms of both a longer time period and a broader coverage of geographic regions than previously studied. Spatiotemporal characteristics of the errors are investigated based on the 24-h forecasts between June 2009 and April 2010, and the 72-h forecasts between May and September 2010. It was found that the biases of both wind and temperature forecasts vary greatly seasonally and diurnally, with dependency on the forecast length, station elevation, geographical location, and meteorological conditions. The temperature showed systematic cold biases during the daytime at all station elevations and warm biases during the nighttime above 1,000 m above sea level (ASL), while below 600 m ASL cold biases occurred during the nighttime. The forecasts of surface wind speed exhibited strong positive biases during the nighttime, while the negative biases were observed in the spring and summer afternoons. The surface wind speed was mostly over-predicted except for the stations located between 1,000 and 2,100 m ASL, for which negative biases were identified for most forecast cycles. The highest wind-speed errors were found over the high terrain and near sea-level stations. The wind-direction errors were relatively large at the high-terrain elevation in the Rocky and Appalachian mountain ranges and the western coastal areas and the error structure exhibited notable diurnal variability.

Implementation of a Silver Iodide Cloud-Seeding Parameterization in WRF. Part II: 3D Simulations of Actual Seeding Events and Sensitivity Tests

AbstractFour cloud-seeding cases over southern Idaho during the 2010/11 winter season have been simulated by the Weather Research and Forecasting (WRF) model using the coupled silver iodide (AgI) cloud-seeding scheme that was described in Part I. The seeding effects of both ground-based and airborne seeding as well as the impacts of model physics, seeding rates, location, timing, and cloud properties on seeding effects have been investigated. The results were compared with those from Part I and showed the following: 1) For the four cases tested in this study, control simulations driven by the Real-Time Four Dimensional Data Assimilation (RTFDDA) WRF forecast data generated more realistic atmospheric conditions and precipitation patterns than those driven by the North America Regional Reanalysis data. Sensitivity experiments therefore used the RTFDDA data. 2) Glaciogenic cloud seeding increased orographic precipitation by less than 1% over the simulation domain, including the Snake River basin, and by up to 5% over the target areas. The local values of the relative precipitation enhancement by seeding were ~20%. Most of the enhancement came from vapor depletion. 3) The seeding effect was inversely related to the natural precipitation efficiency but was positively related to seeding rates. 4) Airborne seeding is generally more efficient than ground-based seeding in terms of targeting, but its efficiency depends on local meteorological conditions. 5) The normalized seeding effects ranged from 0.4 to 1.6 under various conditions for a certain seeding event.

Simultaneous nested modeling from the synoptic scale to the LES scale for wind energy applications

This paper describes an advanced multi-scale weather modeling system, WRF–RTFDDA–LES, designed to simulate synoptic scale (∼2000 km) to small- and micro-scale (∼100 m) circulations of real weather in wind farms on simultaneous nested grids. This modeling system is built upon the National Center for Atmospheric Research (NCAR) community Weather Research and Forecasting (WRF) model. WRF has been enhanced with the NCAR Real-Time Four-Dimensional Data Assimilation (RTFDDA) capability. FDDA is an effective data assimilation algorithm, which is capable of assimilating diverse weather measurements on model grids and seamlessly providing realistic mesoscale weather forcing to drive a large eddy simulation (LES) model within the WRF framework. The WRF based RTFDDA LES modeling capability is referred to as WRF–RTFDDA–LES. In this study, WRF–RTFDDA–LES is employed to simulate real weather in a major wind farm located in northern Colorado with six nested domains. The grid sizes of the nested domains are 30, 10, 3.3, 1.1, 0.370 and 0.123 km, respectively. The model results are compared with wind–farm anemometer measurements and are found to capture many intra-farm wind features and microscale flows. Additional experiments are conducted to investigate the impacts of subgrid scale (SGS) mixing parameters and nesting approaches. This study demonstrates that the WRF–RTFDDA–LES system is a valuable tool for simulating real world microscale weather flows and for development of future real-time forecasting system, although further LES modeling refinements, such as adaptive SGS mixing parameterization and wall-effect modeling, are highly desired.

Model evaluations for winter orographic clouds with observations

The Real-Time Four-Dimensional Data Assimilation (RT-FDDA) system is used for orographic snowpack enhancement. The model has three nested domains with the grid spacing of 18, 6 and 2 km. To evaluate the simulations of winter orographic clouds and precipitation, comparisons are made between model simulations and observations to determine how the model simulates the cloud distribution, cloud height, cloud vertical profiles and snow precipitation. The simulated results of the 02:00 UTC cycling with 2-km resolution are used in the comparison. The observations include SNOTEL, ceilometer, sounding and satellite data, from the ground to air. The verification of these observations indicates that the Weather Research and Forecast (WRF) RT-FDDA system provides good simulations. It is better to use data within the forecast period of 2–16 h simulations. Although the horizontal wind component near the ground has some bias, and the simulated clouds are a little higher and have a little more coverage than observed, the simulated precipitation is a little weaker than observed. The results of the comparison show that the WRF RT-FDDA model provides good simulations and can be used in orographic cloud seeding.

A Wind Power Forecasting System to Optimize Grid Integration

Wind power forecasting can enhance the value of wind energy by improving the reliability of integrating this variable resource and improving the economic feasibility. The National Center for Atmospheric Research (NCAR) has collaborated with Xcel Energy to develop a multifaceted wind power prediction system. Both the day-ahead forecast that is used in trading and the short-term forecast are critical to economic decision making. This wind power forecasting system includes high resolution and ensemble modeling capabilities, data assimilation, now-casting, and statistical postprocessing technologies. The system utilizes publicly available model data and observations as well as wind forecasts produced from an NCAR-developed deterministic mesoscale wind forecast model with real-time four-dimensional data assimilation and a 30-member model ensemble system, which is calibrated using an Analogue Ensemble Kalman Filter and Quantile Regression. The model forecast data are combined using NCAR's Dynamic Integrated Forecast System (DICast). This system has substantially improved Xcel's overall ability to incorporate wind energy into their power mix.