Three-dimensional Variational Assimilation System Research Articles

Hydrological Verification of Two Rainfall Short-Term Forecasting Methods with Floods Anticipation Perspective

Abstract Flood forecasting remains a significant challenge, particularly when dealing with basins characterized by small drainage areas (i.e., 103 km2 or lower with response time in the range 0.5–10 h) especially because of the rainfall prediction uncertainties. This study aims to investigate the performances of streamflow predictions using two short-term rainfall forecast methods. These methods utilize a combination of a nowcasting extrapolation algorithm and numerical weather predictions by employing a three-dimensional variational assimilation system, and nudging assimilation techniques, meteorological radar, and lightning data that are frequently updated, allowing new forecasts with high temporal frequency (i.e., 1–3 h). A distributed hydrological model is used to convert rainfall forecasts into streamflow prediction. The potential of assimilating radar and lightning data, or radar data alone, is also discussed. A hindcast experiment on two rainy periods in the northwest region of Italy was designed. The selected skill scores were analyzed to assess their degradation with increasing lead time, and the results were further aggregated based on basin dimensions to investigate the catchment integration effect. The findings indicate that both rainfall forecast methods yield good performance, with neither definitively outperforming the other. Furthermore, the results demonstrate that, on average, assimilating both radar and lightning data enhances the performance.

Impact of Radar and Surface Data Assimilation on the Forecast of a Nocturnal Squall Line in the Yangtze–Huaihe River

The impact of radar and surface data assimilation on the forecast of a nocturnal squall line initiated above the stable boundary layer in the Yangtze–Huaihe River is investigated by the Weather Research and Forecasting (WRF) model and its three-dimensional variational assimilation system (WRFDA 3DVar). Results show that the assimilation of radar and surface data can improve the prediction of the convection initiation time, height and vertical ascending motion during the early stage of the squall line formation by adjusting the thermodynamic structure, circulation patterns, water vapor conditions and hydrometeor mixing ratios. Although the radar and surface data assimilation can improve the forecast of the location of the squall line to a certain extent, the squall line is stronger in the radar data assimilation than that in the surface data assimilation, leading to stronger radar reflectivity and heavier precipitation. The assimilation of both radar and surface data has a more positive impact on the forecast skill than the assimilation of either type of data. Moreover, during the mature stage of the squall line, radar and surface data assimilation can enhance the intensity of the surface cold pool. Specifically, radar data assimilation or assimilating the two data simultaneously can produce a stronger cold pool than only assimilating surface data, which is more conducive to the maintenance and development of the squall line.

Evaluation on Assimilation Application of Yunhai-2 Occultation Data in Regional Numerical Weather Prediction Model

Based on the WRF (Weather Research and Forecasting) model and GSI (Gridpoint Statistical Interpolation analysis system) three-dimensional variational assimilation system, regional assimilation and prediction experiments for China’s autonomic Yunhai-2 occultation data were performed for the first time in May 2019. The results of the experiments demonstrated that after assimilating Yunhai-2 occultation data, the improvement for the wind and temperature fields is mainly reflected in the middle and later stages of the forecast, while the improvement for the humidity field is witnessed through the entire forecast period. The results also showed that the improvement degree of the wind, temperature, and humidity fields tends to be consistent with the extension of forecast time. It was found that the improvement for the wind and temperature fields is mainly reflected in the middle layer of the model, while the improvement for the water vapor mixing ratio is mainly reflected in the middle and lower layers of the model. Thus, assimilating Yunhai-2 occultation data can reasonably adjust the potential height, humidity, temperature, and wind fields in the model, thereby improving the precipitation forecast results.

Development of a three-dimensional variational assimilation system for lidar profile data based on a size-resolved aerosol model in WRF–Chem model v3.9.1 and its application in PM<sub>2.5</sub> forecasts across China

Abstract. The authors developed a three-dimensional variational (3-DVAR) aerosol extinction coefficient (AEC) and aerosol mass concentration (AMC) data assimilation (DA) system for aerosol variables in the Weather Research and Forecasting–Chemistry (WRF–Chem) model with the WRF–Chem using the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) scheme. They establish an AEC observation operator and its corresponding adjoint based on the Interagency Monitoring of Protected Visual Environments (IMPROVE) equation and investigate the use of lidar AEC and surface AMC DA to forecast mass concentration (MC) profiles of PM2.5 (particulate matter with an aerodynamic diameter of less than 2.5 µm) across China. Two sets of data were assimilated: AEC profiles captured by five conventional Mie scattering lidars (positioned in Beijing, Shijiazhuang, Taiyuan, Xuzhou, and Wuhu) and PM2.5 and PM10 MC data obtained from over 1500 ground environmental monitoring stations across China. Three DA experiments (i.e., a PM2.5 (PM10) DA experiment, a lidar AEC DA experiment, and a simultaneous PM2.5 (PM10) and lidar AEC DA experiment) with a 12 h assimilation period and a 24 h forecast period were conducted. The PM2.5 (PM10) DA reduced the root mean square error (RMSE) of the surface PM2.5 MC in the initial field of the model by 38.6 µg m−3 (64.8 %). When lidar AEC data were assimilated, this reduction was 10.5 µg m−3 (17.6 %), and a 38.4 µg m−3 (64.4 %) reduction occurred when the two data sets were assimilated simultaneously, although only five lidars were available within the simulation region (approximately 2.33 million km2 in size). The RMSEs of the forecasted surface PM2.5 MC 24 h after the DA period in the three DA experiments were reduced by 6.1 µg m−3 (11.8 %), 1.5 µg m−3 (2.9 %), and 6.5 µg m−3 (12.6 %), respectively, indicating that the assimilation and hence the optimization of the initial field have a positive effect on the PM2.5 MC forecast performance over a period of 24 h after the DA period.

Data assimilation of GPS-ZTD into the RAMS model through 3D-Var: preliminary results at the regional scale

The knowledge of water vapour distribution is a key element in atmospheric modeling and considerable information, also at the local scale, can be derived from the GPS-ZTD (global positioning system-Zenith total delay) data.This paper shows the assimilation of GPS-ZTD data into the RAMS@ISAC (Regional Atmospheric Modeling System at Institute of Atmospheric Sciences and Climate of the National Research Council) to improve the representation of the water vapour in the meteorological model.The data assimilation system is based on 3D-Var (three-dimensional variational assimilation system) and it is applied to a network of 29 receivers located within the Lazio Region, Central Italy. All collected data are processed using the PPP (precise point positioning) method through RTKLIB, an open source program package for GNSS (Global Navigation Satellite Systems) Positioning. Among the GPS receivers, three are single frequency receivers, able to acquire L1 frequency only, so that it is necessary a preliminary reconstruction of L2 synthetic observations, which is achieved by a new original ground-based augmentation strategy. Results show remarkably that the single frequency receivers can be used the same way as geodetic receivers.The RAMS@ISAC is run at 4 km horizontal resolution over central Italy and is nested, using one-way nesting, into a 10 km horizontal resolution run of the same model. The experiment was performed along to two months, from 28 July to 28 September 2017.Results show that the GPS-ZTD data, assimilated by 3D-Var, have an important impact on the analysis of the water vapour field and the RMSE of ZTD and IWV (vertically integrated water vapour) is roughly halved for the analysis compared to the background.The impact of the GPS-ZTD data assimilation is also evaluated for the very short term (VSF) forecast (1–3 h), obtaining an improvement of the ZTD and IWV RMSE for all three hours of forecast.

Assimilation of Doppler Weather Radar data with a regional WRF-3DVAR system: Impact of control variables on forecasts of a heavy rainfall case

Short-term precipitation forecasts from numerical weather prediction models are a vital source of information for real-time flood forecasting systems. Previous studies show that assimilation of Doppler Weather Radar (DWR) observations significantly improves the forecast skill of short-term precipitation. However, the variational assimilation methods used for DWR assimilation are sensitive to the selection of control variable options in background error statistics. In this study, the impact of control variable choices in assimilating DWR observations for improving the forecast of heavy rainfall event is analysed. For this purpose radar reflectivity and radial velocity, observations are assimilated using stream function velocity potential (ψχ) and horizontal wind components (uv) control variable options in Weather Research and Forecast model – 3DVAR (three-dimensional variational assimilation system). The results show that DWR assimilation using uv control variable option has improved the skill of first 12 h of high intensity precipitation forecasts.

Impact of Megha-Tropiques SAPHIR radiances in T574L64 global data assimilation and forecasting system at NCMRWF

ABSTRACTThe Sounder for Probing Vertical Profiles of Humidity (SAPHIR) is a sounding instrument of Megha-Tropiques (Indo-French joint satellite); launched by the Indian Space Research Organization on 12 October 2011 with six channels near the absorption band of water vapour at 183 GHz. In the framework of this work, the assimilation scheme has been first modified to enable the SAPHIR radiance observations being used as additional observation type, and second, a methodology has been prepared to remove the radiance pixels significantly affected by clouds. The impact of SAPHIR radiances on analysis as well as forecasts of the National Centre for Medium Range Weather Forecasting-Global Forecast System (NGFS) at T574L64 resolution has been investigated through data assimilation. Measurements from SAPHIR are incorporated into the Gridpoint Statistical Interpolation three-dimensional variational assimilation system to provide the improved initial conditions. To find out the impact, analysis/forecast cycling experiments with and without SAPHIR radiances are performed during the period 10–29 November 2013. The impact of the improvement in term of root mean square error has been clearly evaluated for five parameters, namely, relative humidity, temperature, wind, geopotential height, and specific humidity. It is demonstrated that the assimilation of SAPHIR observations results in a considerable improvement for the five parameters over the global region. During the study period, two tropical cyclones (HELEN, 19–22 November and LEHAR, 23–28 November) were formed over the North Indian Ocean. Impact on specific humidity and track forecast errors of tropical cyclone are also examined. Overall, initial results show the usefulness of SAPHIR radiances in the NGFS.

Evaluation of two momentum control variable schemes and their impact on the variational assimilation of radarwind data: Case study of a squall line

Different choices of control variables in variational assimilation can bring about different influences on the analyzed atmospheric state. Based on the WRF model’s three-dimensional variational assimilation system, this study compares the behavior of two momentum control variable options—streamfunction velocity potential (ψ–χ) and horizontal wind components (U–V)—in radar wind data assimilation for a squall line case that occurred in Jiangsu Province on 24 August 2014. The wind increment from the single observation test shows that the ψ–χ control variable scheme produces negative increments in the neighborhood around the observation point because streamfunction and velocity potential preserve integrals of velocity. On the contrary, the U–V control variable scheme objectively reflects the information of the observation itself. Furthermore, radial velocity data from 17 Doppler radars in eastern China are assimilated. As compared to the impact of conventional observation, the assimilation of radar radial velocity based on the U–V control variable scheme significantly improves the mesoscale dynamic field in the initial condition. The enhanced low-level jet stream, water vapor convergence and low-level wind shear result in better squall line forecasting. However, the ψ–χ control variable scheme generates a discontinuous wind field and unrealistic convergence/divergence in the analyzed field, which lead to a degraded precipitation forecast.

Variational assimilation of satellite cloud water/ice path and microphysics scheme sensitivity to the assimilation of a rainfall case

Hydrometeor variables (cloud water and cloud ice mixing ratios) are added into the WRF three-dimensional variational assimilation system as additional control variables to directly analyze hydrometeors by assimilating cloud observations. In addition, the background error covariance matrix of hydrometeors is modeled through a control variable transform, and its characteristics discussed in detail. A suite of experiments using four microphysics schemes (LIN, SBU-YLIN, WDM6 and WSM6) are performed with and without assimilating satellite cloud liquid/ice water path. We find analysis of hydrometeors with cloud assimilation to be significantly improved, and the increment and distribution of hydrometeors are consistent with the characteristics of background error covariance. Diagnostic results suggest that the forecast with cloud assimilation represents a significant improvement, especially the ability to forecast precipitation in the first seven hours. It is also found that the largest improvement occurs in the experiment using the WDM6 scheme, since the assimilated cloud information can sustain for longer in this scheme. The least improvement, meanwhile, appears in the experiment using the SBU-YLIN scheme.

Assimilation of HY-2A scatterometer sea surface wind data in a 3DVAR data assimilation system—A case study of Typhoon Bolaven

The scatterometer (SCAT) on-board China’s HY-2A satellite has the capability to provide high resolution wind vector information over the global ocean surface. These wind vector data produced by the HY-2A scatterometer (HY-2A SCAT) are available to the data assimilation system with real-time information of high accuracy. In this paper, two experiments are designed to investigate the impact of HY-2A SCAT data in the three-dimensional variational assimilation system for the Weather Research and Forecast model (WRF 3DVAR). The powerful Typhoon Bolaven, which struck South Korea in August 2012, is selected for this case study. The results clearly demonstrate that HY-2A SCAT data can effectively complement the scarce observations over the ocean surface and improve the prediction of the wind and pressure fields of a typhoon. The case study of Typhoon Bolaven exhibits the significant and positive impact of HY-2A SCAT data on the numerical prediction of the tropical cyclone track.

Assimilation of clear sky Atmospheric Infrared Sounder radiances in short-term regional forecasts using community models

Regional assimilation experiments of clear-sky Atmospheric Infrared Sounder (AIRS) radiances were performed using the gridpoint statistical interpolation three-dimensional variational assimilation system coupled to the weather research and forecasting model. The data assimilation system and forecast model used in this study are separate community models; it cannot be assumed that the coupled systems work optimally. Tuning was performed on the data assimilation system and forecast model. Components tuned included the background error covariance matrix, the satellite radiance bias correction, the quality control procedures for AIRS radiances, the forecast model resolution, and the infrared channel selection. Assimilation metrics and diagnostics from the assimilation system were used to identify problems when combining separate systems. Forecasts initiated from analyses after assimilation were verified with model analyses, rawinsondes, nonassimilated satellite radiances, and 24 h–accumulated precipitation. Assimilation of clear sky AIRS radiances showed the largest improvement in temperature and radiance brightness temperature bias when compared with rawinsondes and satellite observations, respectively. Precipitation skill scores displayed minor changes with AIRS radiance assimilation. The 00 and 12 coordinated universal time (UTC) forecasts were typically of better quality than the 06 and 18 UTC forecasts, possibly due to the amount of AIRS data available for each assimilation cycle.

Assimilation of temperature and salinity using isotropic and anisotropic recursive filters in Tropic Pacific

A data assimilation scheme used in the updated Ocean three-dimensional Variational Assimilation System (OVALS), OVALS2, is described. Based on a recursive filter (RF) to estimate the background error covariance (BEC) over a predetermined scale, this new analysis system can be implemented with anisotropic and isotropic BECs. Similarities and differences of these two BEC schemes are briefly discussed and their impacts on the model simulation are also investigated. An idealized experiment demonstrates the ability of the updated analysis system to construct different BECs. Furthermore, a set of three years experiments is implemented by assimilating expendable bathythermograph (XBT) and ARGO data into a Tropical Pacific circulation model. The TAO and WOA01 data are used to validate the assimilation results. The results show that the model simulations are substantially improved by OVALS2. The inter-comparison of isotropic and anisotropic BEC shows that the corresponding temperature and salinity produced by the anisotropic BEC are almost as good as those obtained by the isotropic one. Moreover, the result of anisotropic RF is slightly closer to WOA01 and TAO than that of isotropic RF in some special area (e.g. the cold tongue area in the Tropic Pacific).

The effects of driver data on the performance of the FinROSE chemistry transport model

In this paper we tested the performance of the FinROSE chemistry transport model for three different datasets from the European Centre for Medium-Range Weather Forecasts (ECMWF). Global middle atmospheric simulations from 1990 to 2005 were done using winds and temperatures from the ECMWF re-analysis (ERA) datasets, the ERA-40, the operational and the ERA-Interim. Analysis data was used in all simulations. The performance of the model for each dataset was analysed using the simulated stratospheric age-of-air and the ascent rate in the tropics. The ERA-40 data produced with a three-dimensional variational assimilation system (3D-Var) resulted in a too strong Brewer–Dobson circulation. The operational analysis produced with a four-dimensional variational assimilation system (4D-Var) gave somewhat improved results, and the new 4D-Var ERA-Interim re-analysis resulted in a much more realistic upward transport. Also the modelled ozone showed better agreement with observations when using the new re-analysis data.