Multi‐resolution incremental 4D‐Var for WRF: Implementation and application at convective scale

Assimilating GNSS PWV and radiosonde meteorological profiles to improve the PWV and rainfall forecasting performance from the Weather Research and Forecasting (WRF) model over the South China

Himawari‐8 is the first launched and operational new‐generation geostationary meteorological satellite. The Advanced Himawari Imager (AHI) on board Himawari‐8 provides continuous high‐resolution observations of severe weather phenomena in space and time. In this study, the capability to assimilate AHI radiances has been developed within the Weather Research and Forecasting (WRF) model's data assimilation system. As the first attempt to assimilate AHI using WRF data assimilation at convective scales, the added value of hourly AHI clear‐sky radiances from three water vapor channels on convection‐permitting (3 km) analyses and forecasts of the “7.19” severe rainstorm that occurred over north China during 18–21 July 2016 was investigated. Analyses were produced hourly, and 24 h forecasts were produced every 6 h. The results showed that improved wind and humidity fields were obtained in analyses and forecasts verified against conventional observations after assimilating AHI water vapor radiances when compared to the control experiment which assimilated only conventional observations. It was also found that the assimilation of AHI water vapor radiances had a clearly positive impact on the rainfall forecast for the first 6 h lead time, especially for heavy rainfall exceeding 100 mm when verified against the observed rainfall. Furthermore, the horizontal and vertical distribution of features in the moisture fields were improved after assimilating AHI water vapor radiances, eventually contributing to a better forecast of the severe rainstorm.

Assimilation of accurate external satellite-based water vapor data has been demonstrated to be able to improve the Numerical Weather Prediction (NWP) model’s forecasting performance. In this study, two types of near-infrared Precipitable Water Vapor (PWV) data from the Ocean and Land Colour Instrument (OLCI) of the Sentinel-3A and -3B satellites are assimilated into the Weather Research and Forecasting (WRF) model to enhance the weather forecasting performance over the South China area. One type is the raw PWV products obtained directly from the Sentinle-3A and -3B satellites; the other type is the Sentinel-3A and -3B PWV calibrated by PWV from Global Navigation Satellite System (GNSS). The Back Propagation Neural Network (BPNN) model is used in the PWV calibration. Correspondingly, two WRF assimilation schemes are adopted to assimilate the Sentinel-3 raw PWV and Sentinel-3 calibrated PWV. Additionally, the WRF without data assimilation scheme is used as the background run to investigate the impact of data assimilation. Three WRF experimental schemes are performed for two periods, i.e., February 2020 (dry period) and July 2020 (wet period). The forecasting performance of these three WRF schemes are evaluated by observations from GNSS, radiosonde, and surface meteorological stations. The comparison results with GNSS PWV show that assimilation of Sentienl-3 calibrated PWV can improve PWV forecasting accuracy by 5.6% for February period for the first 6 h after data assimilation. Evaluated by radiosonde profiles, the assimilation of Sentinel-3 both raw and calibrated PWV can improve WRF humidity and temperature forecasting accuracy over the July period. The accuracy improvement can be up to 9.2% (at 2 km to 3 km of altitude) for water vapor mixing ratio and 6.0% for temperature (at 4 km to 5 km of altitude). The WRF rainfall evaluation result has been validated by rainfall observations from meteorological stations and it shows that the assimilation of Sentinel-3 PWV can improve the rainfall forecasting performance over the July period. The rainfall forecasting success rates of three schemes of assimilating no data, Sentinel-3 raw PWV, and Sentinel-3 calibrated PWV are 69.4%, 69.6%, and 71.1%, respectively, over the July 2020 in the South China region.

<p>The moisture recycling is defined as the fraction of precipitation over a delimited region that comes from the evaporation over that region. Its importance lies in the fact that it is an approximated measurement of a regional feedback between the atmosphere and the surface. Thus, this study estimates the spatio-temporal distribution of moisture recycling over the Iberian Peninsula (IP), and focuses on the impact of the use of 3DVAR data assimilation during the modeling stage.</p><p>For that purpose, two different simulations were run using the Weather and Research Forecasting (WRF) model with a horizontal resolution of 15 km over the IP. The first simulation (WRF N) was nested inside ERA-Interim as usual in numerical downscaling exercises, with information passed to the domain through the boundaries. The second run (WRF D) presents the same configuration as WRF N, but it also includes 3DVAR data assimilation step every six hours (at 00, 06, 12 and 18 UTC). Sea surface temperature was updated daily, and observations in PREPBUFR format included in the NCEP ADP Global Upper Air and Surface Weather Observations dataset were used for the data assimilation step. Only those inside a 120-minute window centered at the analysis times were assimilated. Both simulations cover the period 2010-2014, but the experiment WRF D was extended later until 2018.</p><p>The lowest values of moisture recycling (around 3 %) are obtained from November to February, while the most remarkable values are observed in spring (around 16 %) in both simulations. The moisture recycling is confined to the southeastern corner of the IP during winter. However, during spring and summer, a gradient of higher values towards the northeastern corner of the IP are observed in both simulations. The differences between simulations, associated to the dryness of the soil in the model, are highlighted during summer and autumn. WRF D presents a lower bias and produces more reliable results because of a better representation of the atmospheric moisture.</p><p>A Cross-Correlation Function (CCF) based analysis was performed for each combination of moisture recycling, accumulated precipitation and mean soil moisture over the IP. For the common period (2010-2014), the results show that the WRF D experiment extends the lifespan of moisture over the IP. The CCF analysis for soil moisture against precipitation also shows an unphysical negative lag (-1 month) for WRF N, whilst for WRF D both variables are simultaneous. For the extended WRF D simulation (2010-2018), it was found that the delay between precipitation and moisture recycling over the IP is five months.</p>

Repeat-pass spaceborne interferometric synthetic aperture radar (InSAR) is commonly used to measure surface deformation; phase delays due to atmospheric water vapour may have significant impact on the accuracy of these measurements. In recent years, there has been a growing interest in using forecasts and analyses from numerical weather prediction (NWP) models – which can provide good estimates of the atmospheric state – to correct for atmospheric phase delays. In this study, three separate estimates of atmospheric water vapour content from NWP output are used in combination with Environmental Satellite (Envisat) Advanced Synthetic Aperture Radar (ASAR) data over the Pearl River Delta region in South China to mitigate atmospheric distortion. The NWP-based estimates are derived from: (1) interpolation of National Centers for Environmental Prediction (NCEP) Final Operational Global Analysis (FNL) data; (2) Weather Research and Forecasting (WRF) model simulations initialized with FNL analysis without additional data assimilation; and (3) WRF simulations initialized with a three-dimensional variational (3DVar) data assimilation system that ingests additional meteorological observations. The accuracy of the atmospheric corrections from these different NWP model outputs is further verified quantitatively with precipitable water vapour (PWV) data from several ground-based global positioning system (GPS) stations in Hong Kong. Inter-comparison shows a good agreement between the PWV derived from the WRF-3DVar simulations and the GPS measurements, suggesting that atmospheric correction by convection-permitting WRF simulations initialized with mesoscale data assimilation may effectively mitigate atmospheric distortion in InSAR measurements, especially for coastal areas.

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.

Raw data collected at a single Global Navigation Satellite System (GNSS) station allow the estimation of the Zenith Total Delay (ZTD) and the tropospheric gradient. In order to make use of such data in Numerical Weather Prediction (NWP) the observation operators must be developed. Our current observation operator for tropospheric gradients is based on a linear combination of ray-traced tropospheric delays (Zus et al., 2022). Although this observation operator is tuned for high speed and precision it remains difficult to be implemented into NWP Data Assimilation (DA) systems. In this contribution we introduce a simple and fast observation operator which is based on the closed-form expression depending on the north–south and east–west horizontal gradients of radio refractivity (Davis et al., 1993). We run the Weather Research and Forecasting (WRF) model (horizontal resolution of 10km) and find that for the considered geographical region (central Europe) and time period (summer season) the root-mean-square deviation between the tropospheric gradients calculated by the fast and original approach is about 0.15 mm. In essence, the observation operator error is non negligible but acceptable for assimilation. In a first step we implemented the developed operator in our experimental DA system (Zus et al., 2019) and run a series of experiments to check the usefulness of the new approach. We present results from this assimilation experiments where we utilize both simulated and real observations. In the next step we will implement the fast observation operator in the WRF DA system in support of the research project EGMAP (Exploitation of GNSS tropospheric gradients for severe weather Monitoring And Prediction).Davis, J., Elgered, G., Niell, A., and Kuehn, K.: Ground-based measurement of gradients in the “wet” radio refractivity of air, Radio Sci., 28, 1003–1018, 1993. Zus, F.; Douša, J.; Kačmařík, M.; Václavovic, P.; Dick, G.; Wickert, J. Estimating the Impact of Global Navigation Satellite System Horizontal Delay Gradients in Variational Data Assimilation. Remote Sens. 2019, 11, 41.Zus, F., Galina, D., and Wickert, J.: Development of a cost efficient observation operator for GNSS tropospheric gradients, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1079

Dual-resolution (DR) hybrid variational-ensemble analysis capability was implemented within the community Weather Research and Forecasting (WRF) Model data assimilation (DA) system, which is designed for limited-area applications. The DR hybrid system combines a high-resolution (HR) background, flow-dependent background error covariances (BECs) derived from a low-resolution ensemble, and observations to produce a deterministic HR analysis. As DR systems do not require HR ensembles, they are computationally cheaper than single-resolution (SR) hybrid configurations, where the background and ensemble have equal resolutions. Single-observation tests were performed to document some characteristics of limited-area DR hybrid analyses. Additionally, the DR hybrid system was evaluated within a continuously cycling framework, where new DR hybrid analyses were produced every 6 h over ~3.5 weeks. In the DR configuration presented here, the deterministic backgrounds and analyses had 15-km horizontal grid spacing, but the 32-member WRF Model–based ensembles providing flow-dependent BECs for the hybrid had 45-km horizontal grid spacing. The DR hybrid analyses initialized 72-h WRF Model forecasts that were compared to forecasts initialized by an SR hybrid system where both the ensemble and background had 15-km horizontal grid spacing. The SR and DR hybrid systems were coupled to an ensemble adjustment Kalman filter that updated ensembles each DA cycle. On average, forecasts initialized from 15-km DR and SR hybrid analyses were not statistically significantly different, although tropical cyclone track forecast errors favored the SR-initialized forecasts. Although additional studies over longer time periods and at finer grid spacing are needed to further understand sensitivity to ensemble perturbation resolution, these results suggest users should carefully consider whether SR hybrid systems are worth the extra cost.

Artificial intelligence (AI) models have demonstrated advancements in computational efficiency and forecast accuracy relative to the Numerical Weather Prediction (NWP), but they are unable to fully represent high-dimensional atmospheric dynamics. Thus, some AI-NWP coupled frameworks have been proposed, such as integrating AI-driven boundary conditions with numerical models to leverage the strengths of both approaches. However, in this coupled framework, ensemble forecasts and associated error propagation and energy dynamics remain under-explored. In this study, an AI-NWP coupled system that also uses the stochastic kinetic energy backscatter scheme (SKEBS) to generate ensemble forecasts is established. Ensemble simulations of Typhoon Yutu (2018) are carried out with the Weather Research and Forecasting (WRF) model employing Pangu-Weather and FuXi forecast data as boundary forcing. The results show that the ensemble WRF_Pangu (WRF_FuXi) improved Yutu’s track forecast by 67% (50%) compared to the traditional physics-based WRF_GFS (Global Forecast System), and reduced its intensity underestimation by about 67% relative to their AI global counterparts. Nonetheless, WRF_FuXi and WRF_Pangu exhibited limited ensemble spread and linear error growth, reflecting deterministic tendencies. Comparison of global and regional experiments show that Pangu-Weather is more physically constrained and thus better aligned with the WRF model for regional applications, while the adaptation of FuXi to the regional model is less robust. Spectral analysis revealed that AI-derived boundaries introduced excessive small-scale energy and underestimated larger-scale energy. The regional model WRF acted as a “conveyor belt”, propagating additive small-scale energy upscale, ultimately overwhelming the stochastic perturbations for ensemble generation. These findings underscore the need to incorporate more physical features into the AI-derived boundary conditions for ensemble forecasting.

To forecast wind speed at hub-height is a challenging task for wind energy application, especially in Japan where the terrain feature is very complex and large fluctuations are observed in surface wind field. In this study, an integrated system to predict the hub-height wind speed has been developed by combining data assimilation and Kalman filter with the high resolution Weather Research and Forecasting (WRF) model. Assimilating the nacelle wind data (quality-controlled) and the Kalman filter algorithm effectively improves accuracy of the WRF model forecast by optimizing initial condition and post-processing the model output, respectively. It is found that the WRF model forecasts can be markedly improved after assimilating the nacelle wind data through the Gridpoint Statistical Interpolation analysis system, with the relative improvements of 34.3%, 23.9%, and 8.8% in ME (mean error), RMSE (root mean square error), and IA (index of agreement), respectively. The implementation of the Kalman filter can significantly reduce ME and RMSE while increases the value of IA as well. Further improvement can be achieved if the Kalman filter and nacelle wind data assimilation are implemented simultaneously. It is observed that the role of the Kalman filter is more dominant for the wind band of rated out speeds, while data assimilation is effective in reducing the random errors and becomes more important in rare or extreme weather conditions. Both data assimilation and Kalman filter modules apply the nacelle wind data which is routinely available, so the system can be easily adopted in different wind farm sites for operational use.

This study is intended to simulate and better understand mean and extreme precipitation over the Loess Plateau (LP) in China using the Weather Research and Forecasting (WRF) model. We performed a long-term (1980–2011) simulation with the WRF model at a 10 km horizontal resolution, forced by the ERA-Interim reanalysis. A series of sensitivity experiments were conducted to investigate the influence of resolution and physical parameterization schemes on the simulation ability of the WRF model over the LP. The region has a dominant semi-arid climate and is adversely affected by extreme precipitation. Results show that the WRF model produced better simulations at a 10 km resolution for the LP with complex terrain than it did at coarser resolutions. WRF simulations for precipitation over the LP were most sensitive to cumulus schemes, moderately sensitive to planetary boundary layer schemes, and less sensitive to microphysics schemes. The WRF model not only adequately captured the spatial distribution of precipitation in the LP but also reproduced its magnitude and variability at different time scales. Moreover, the WRF model captured 73% of the observed extreme precipitation events with an intensity of more than 12 mm day− 1, which cause substantial soil erosion in the LP. Although it was unable to capture 27% of the observed events, the WRF model reasonably reproduced ERA-Interim extreme precipitation and its dynamic processes. These analyses strongly suggest that the biased extreme precipitation generated with the WRF model most likely resulted from inaccurate ERA-Interim forcing data. This study provides a better understanding of extreme precipitation simulations as well as insight into predictions of soil erosion in the LP.

Atmospheric wet delay caused by Precipitable Water Vapor (PWV) significantly impacts the performance of many geodetic surveying systems such as Global Navigation Satellite System (GNSS). In this study, we use wet delay corrections forecast by the Weather Research and Forecasting (WRF) model to enhance GNSS Precise Point Positioning (PPP) during two observation periods with two different weather conditions, that is, period 1: March 01 to 14, 2020 (average PWV: 23.5 kg/m2) and period 2: June 02 to 15, 2020 (flooding weather with average PWV: 55.6 kg/m2), over the South China. PWV data from 277 to 263 GNSS stations are assimilated into WRF model to enhance the WRF water vapor forecasting capability for period 1 and period 2, respectively. Wet delay corrections from two different WRF configurations, that is, WRF no data assimilation and WRF with assimilation of GNSS PWV, are used to augment the PPP. Totally, eight WRF‐enhanced PPP schemes are tested. The results show that WRF‐enhanced PPP schemes generally have a better positioning performance in the up component than traditional PPP. After using WRF wet delay corrections, for static mode, the vertical positioning accuracy is improved by 14.6% and 33.7% for period 1 and period 2, respectively. The corresponding convergence time are reduced by 41.8% and 25.0% for period 1 and period 2, respectively. For kinematic mode, the positioning accuracy improvements in the up component reach 13.8% and 19.0% for period 1 and period 2, respectively. The kinematic PPP convergence time is reduced by up to 8.2% for period 1.

This paper investigates the accuracy of weather research and forecasting by improving coding for solar radiation forecasting for location in Dili Timor Leste. Weather Research and Forecasting (WRF) model version 3.9.1 is used in this study for improvement purposes. The shortwave coding of WRF is used to improve in order to decrease error simulation. The importance of improving WRF coding at a specific region will reduce the bias and root mean square root when comparing to the observed data. This study uses high resolution based on the WRF modeling to stabilize the performance of forecasting. The decrease in error performance will be expected to enhance the value of renewable energy. The results show the root mean square error of the WRF default is 233 W/m2 higher compared to 205 W/m2 from the WRF improvement model. In addition, the Mean Bias Error (MBE) of the WRF default is obtained value 0.06 higher than 0.03 from the WRF improvement in rainy days. Meanwhile, on sunny days, the performance Root Mean Square Error (RMSE) of WRF default is 327 W/m2 higher than 223 W/m2 from the WRF improvement. The MBE of WRF improvement obtained 0.13 lower compared to 0.21 of WRF default coding. Finally, this study concludes that improving the shortwave code under the WRF model can decrease the error performance of the WRF simulation for local weather forecasting.

This study focuses on the sensitivity of tropical cyclones (TCs) simulations to physics parametrization scheme for TCs in the Bay of Bengal (BOB). The goal of this study was to arrive at the optimum set of schemes for the BOB region to increase forecast skill. Four TCs, namely Khaimuk, Laila, Jal and Thane have been simulated through the weather research and forecasting (WRF) model with all the physics parametrization schemes available in WRF, and the optimum set of schemes is arrived at. The analysis shows the cumulus, microphysics and planetary boundary layer parameterizations exert a very significant influence on the TC simulations than land surface, short-wave radiation and long-wave radiation parameterizations. With this optimum set of physics schemes, the impact of assimilation of National Centers for Environmental Prediction Automatic Data Processing upper air observations data in the TC simulations has been studied by using three-dimensional variational (3DVAR) data assimilation technique. The control run (without assimilation) and the 3DVAR-simulated tracks and maximum sustained wind speed have been compared with the Joint Typhoon Warning Center observed tracks and wind data. The model-simulated precipitation is validated with Tropical Rainfall Measuring Mission 2A12 surface rain rate and 3B42 daily accumulated rain data. Bias score and equitable threat score have been evaluated for both instantaneous rain rate and 72-h accumulated rain.

Recent studies have shown the unique value of satellite-observed land surface thermal infrared (TIR) information (e.g., skin temperature) and the feasibility of assimilating land surface temperature (LST) into land surface models (LSMs) to improve the simulation of land-atmosphere water and energy exchanges. In this study, two different types of LST assimilation techniques are implemented and the benefits from the techniques are compared. One of the techniques is to directly assimilate LST using ensemble Kalman filter (EnKF) data assimilation (DA) utilities. The other is to use the Atmosphere-Land Exchange Inversion model (ALEXI) as an “observation operator” that converts LST retrievals into the soil moisture (SM) proxy based on the ratio of actual to potential evapotranspiration (fPET), which is then assimilated into an LSM. While most current studies have shown some success in both directly the assimilating LST and assimilating ALEXI SM proxy into offline LSMs, the potential impact of the assimilation of TIR information through coupled numerical weather prediction (NWP) models is unclear. In this study, a semi-coupled Land Information System (LIS) and Weather Research and Forecast (WRF) system is employed to assess the impact of the two different techniques for assimilating the TIR observations from NOAA GOES satellites on WRF model forecasts. The NASA LIS, equipped with a variety of LSMs and advanced data assimilation tools (e.g., the ensemble Kalman Filter (EnKF)), takes atmospheric forcing data from the WRF model run, generates updated initial land surface conditions with the assimilation of either LST- or TIR-based SM and returns them to WRF for initializing the forecasts. The WRF forecasts using the daily updated initializations with the TIR data assimilation are evaluated against ground weather observations and re-analysis products. It is found that WRF forecasts with the LST-based SM assimilation have better agreement with the ground weather observations than those with the direct LST assimilation or without the land TIR data assimilation.