Variational Data Assimilation Technique Research Articles

Accurate initial field estimation for weather forecasting with a variational constrained neural network

Weather forecasting is crucial for scientific research and society. Recently, deep learning (DL) methods have achieved significant advancements in medium-range weather forecasting. However, they generally depend on the initial fields generated by the computationally expensive four-dimensional variational (4DVar) data assimilation (DA) technique, which limits their real-time applicability in multivariate three-dimensional (3D) weather forecasting. Here we propose 4DVarFormer by exploring the potential of integrating the 4DVar constraint into an attention-based neural network. 4DVarFormer eliminates the need for background error covariance statistics and the complex adjoint model development. It can generate multivariate 3D weather states within 0.37 s. Moreover, 4DVarFormer can capture inter-variable relationships, allowing the assimilation of observed variables to correct unobserved variables. Hence, medium-range forecasts initiated by 4DVarFormer outperform those of DL-based DA methods and achieve performance comparable to the forecasts initiated by ERA5 reanalyses. These promising findings contribute to future advancements in integrated end-to-end DL weather forecasting systems.

Integrating 3DVAR and ANN for inversion optimization of physical models of fuel-assembly bowing

This study introduces a novel inversion optimization method for the core physical model of fuel-assembly bowing, integrating measurement data based on three-dimensional variational (3DVAR) data assimilation and artificial neural network (ANN) techniques. Recognizing the deviation between theoretical physical models and actual core conditions due to factors like uneven coolant distribution and fuel-assembly bowing, this work aims to enhance model reliability and simulation accuracy. By generating a dataset through random perturbation and employing ANN for training, the method establishes a relationship between the core physical model and model-simulated values corresponding to measurement data. The construction of the cost function, essential for optimizing the inversion process, is executed using 3DVAR. To verify its validity, the method is implemented, and an inversion optimization process is developed using our self-developed Bamboo-C code system. The optimization process, validated through application in a commercial pressurized water reactor (PWR) in a nuclear power plant (NPP) in China, demonstrates a significant reduction in the relative errors of power distribution, thus affirming the method’s efficacy in achieving a more realistic model of fuel-assembly bowing. The integration of measurement data into the optimization process addresses the critical need for accurate core physical models in nuclear engineering, offering a promising avenue for enhancing the safety and efficiency of nuclear reactor operations.

How frequent and which variables of automatic weather station data should be assimilated into WRF-3DVar model? A case study of a squall line event in Beijing

Accurate precipitation forecasting is one of the most challenging problems in mesoscale Numerical Weather Prediction (NWP) models. The utilization of 3-Dimensional Variational (3DVar) Data Assimilation technique based on automatic weather station (AWS) data can significantly enhance the accuracy of precipitation simulations and forecasts using the Weather Research and Forecasting (WRF) model. However, the impacts of different assimilation frequencies and meteorological variables on the accuracy of precipitation forecasts remain unclear. This study comprehensively evaluated the impact of different assimilation frequencies and meteorological variables on precipitation forecasts, as illustrated by a case study of a squall line event on August 2, 2017, in Beijing. Eight experiments were conducted by assimilating different combinations of meteorological variables (air pressure, temperature, relative humidity, wind speed, and wind direction) at various time intervals (1 h, 3 h, and 6 h). The results indicated that the WRF model roughly simulated the evolution of this event but overestimated the precipitation amount, accompanied by a large deviation in precipitation areas. Assimilating detailed AWS data significantly improves the model performance in precipitation forecasts. The experiment assimilating all variables at a 3 h interval yielded the most accurate forecasts, with the maximum threat score (TS) increasing from 0.02 to 0.56. Higher assimilation frequencies do not guarantee a better performance. In practice, a 3 h assimilation frequency emerges as an optimal choice for AWS data assimilation. The assimilation of various variables enhanced precipitation forecasts to varying degrees, with the optimum results achieved when all variables were assimilated. Wind speed and direction were the most significant factors in dynamically enhancing the simulation of precipitation areas. Relative humidity and temperature influenced the precipitation intensity by affecting the evolution of convective precipitation thermodynamically. The findings of this study can contribute to the development of AWS data assimilation strategies for the WRF-3DVar model, thereby enhancing the precipitation forecast accuracy.

Three-Dimensional Variational Multi-Doppler Wind Retrieval over Complex Terrain

Abstract The interaction of airflow with complex terrain has the potential to significantly amplify extreme precipitation events and modify the structure and intensity of precipitating cloud systems. However, understanding and forecasting such events is challenging, in part due to the scarcity of direct in situ measurements. Doppler radar can provide the capability to monitor extreme rainfall events over land, but our understanding of airflow modulated by orographic interactions remains limited. The SAMURAI software is a three-dimensional variational data assimilation (3DVAR) technique that uses the finite element approach to retrieve kinematic and thermodynamic fields. The analysis has high fidelity to observations when retrieving flows over a flat surface, but the capability of imposing topography as a boundary constraint is not previously implemented. Here, we implement the immersed boundary method (IBM) as pseudo-observations at their native coordinates in SAMURAI to represent the topographic forcing and surface impermeability. In this technique, neither data interpolation onto a Cartesian grid nor explicit physical constraint integration during the cost function minimization is needed. Furthermore, the physical constraints are treated as pseudo-observations, offering the flexibility to adjust the strength of the boundary condition. A series of observing simulation sensitivity experiments (OSSEs) using a full-physics model and radar emulator simulating rainfall from Typhoon Chanthu (2021) over Taiwan are conducted to evaluate the retrieval accuracy and parameter settings. The OSSE results show that the strength of the IBM constraints can impact the overall wind retrievals. Analysis from real radar observations further demonstrates that the improved retrieval technique can advance scientific analyses for the underlying dynamics of orographic precipitation using radar observations.

Ensemble optimal interpolation for adjoint-free biogeochemical data assimilation.

Advanced marine ecosystem models can contain more than 100 biogeochemical variables, making data assimilation for these models a challenging prospect. Traditional variational data assimilation techniques like 4dVar rely on tangent linear and adjoint code, which can be difficult to create for complex ecosystem models with more than a few dozen variables. More recent hybrid ensemble-variational data assimilation techniques use ensembles of model forecasts to produce model statistics and can thus avoid the need for tangent linear or adjoint code. We present a new implementation of a four-dimensional ensemble optimal interpolation (4dEnOI) technique for use with coupled physical-ecosystem models. Our 4dEnOI implementation uses a small ensemble, and spatial and variable covariance localization to create reliable flow-dependent statistics. The technique is easy to implement, requires no tangent linear or adjoint code, and is computationally suitable for advanced ecosystem models. We test the 4dEnOI implementation in comparison to a 4dVar technique for a simple marine ecosystem model with 4 biogeochemical variables, coupled to a physical circulation model for the California Current System. In these tests, our 4dEnOI reference implementation performs similarly well to the 4dVar benchmark in lowering the model observation misfit. We show that the 4dEnOI results depend heavily on covariance localization generally, and benefit from variable localization in particular, when it is applied to reduce the coupling strength between the physical and biogeochemical model and the biogeochemical variables. The 4dEnOI results can be further improved by small modifications to the algorithm, such as multiple 4dEnOI iterations, albeit at additional computational cost.

Inverse Modeling of Formaldehyde Emissions and Assessment of Associated Cumulative Ambient Air Exposures at Fine Scale

Among air toxics, formaldehyde (HCHO) is an important contributor to urban cancer risk. Emissions of HCHO in the United States are systematically under-reported and may enhance atmospheric ozone and particulate matter, intensifying their impacts on human health. During the 2021 Michigan-Ontario Ozone Source Experiment (MOOSE), mobile real-time (~1 s frequency) measurements of ozone, nitrogen oxides, and organic compounds were conducted in an industrialized area in metropolitan Detroit. The measured concentrations were used to infer ground-level and elevated emissions of HCHO, CO, and NO from multiple sources at a fine scale (400 m horizontal resolution) based on the 4D variational data assimilation technique and the MicroFACT air quality model. Cumulative exposure to HCHO from multiple sources of both primary (directly emitted) and secondary (atmospherically formed) HCHO was then simulated assuming emissions inferred from inverse modeling. Model-inferred HCHO emissions from larger industrial facilities were greater than 1 US ton per year while corresponding emission ratios of HCHO to CO in combustion sources were roughly 2 to 5%. Moreover, simulated ambient HCHO concentrations depended significantly on wind direction relative to the largest sources. The model helped to explain the observed HCHO concentration gradient between monitoring stations at Dearborn and River Rouge in 2021.

High-resolution inverse modelling of European CH4 emissions using the novel FLEXPART-COSMO TM5 4DVAR inverse modelling system

Abstract. We present a novel high-resolution inverse modelling system (“FLEXVAR”) based on FLEXPART-COSMO back trajectories driven by COSMO meteorological fields at 7 km×7 km resolution over the European COSMO-7 domain and the four-dimensional variational (4DVAR) data assimilation technique. FLEXVAR is coupled offline with the global inverse modelling system TM5-4DVAR to provide background mole fractions (“baselines”) consistent with the global observations assimilated in TM5-4DVAR. We have applied the FLEXVAR system for the inverse modelling of European CH4 emissions in 2018 using 24 stations with in situ measurements, complemented with data from five stations with discrete air sampling (and additional stations outside the European COSMO-7 domain used for the global TM5-4DVAR inversions). The sensitivity of the FLEXVAR inversions to different approaches to calculate the baselines, different parameterizations of the model representation error, different settings of the prior error covariance parameters, different prior inventories, and different observation data sets are investigated in detail. Furthermore, the FLEXVAR inversions are compared to inversions with the FLEXPART extended Kalman filter (“FLExKF”) system and with TM5-4DVAR inversions at 1∘×1∘ resolution over Europe. The three inverse modelling systems show overall good consistency of the major spatial patterns of the derived inversion increments and in general only relatively small differences in the derived annual total emissions of larger country regions. At the same time, the FLEXVAR inversions at 7 km×7 km resolution allow the observations to be better reproduced than the TM5-4DVAR simulations at 1∘×1∘. The three inverse models derive higher annual total CH4 emissions in 2018 for Germany, France, and BENELUX compared to the sum of anthropogenic emissions reported to UNFCCC and natural emissions estimated from the Global Carbon Project CH4 inventory, but the uncertainty ranges of top-down and bottom-up total emission estimates overlap for all three country regions. In contrast, the top-down estimates for the sum of emissions from the UK and Ireland agree relatively well with the total of anthropogenic and natural bottom-up inventories.

Medicane Ianos: 4D-Var Data Assimilation of Surface and Satellite Observations into the Numerical Weather Prediction Model WRF

This work investigates extreme weather events such as the onset of medicanes, which can cause severe socioeconomic impacts, along with their predictability. In order to accurately forecast such events, the Weather Research and Forecasting (WRF) model and its state-of-the-art data assimilation modeling framework (WRFDA) were set up to produce high-resolution forecasts for the case study of Medicane Ianos, which affected Greece between 17 and 19 September 2020. Information from weather stations and the satellite precipitation product IMERG was blended with the background model information from the Global Forecast System (GFS) using the 4D variational data assimilation (4D-Var) technique. New fields in an 18 km spatial resolution domain covering Europe were generated and utilized as improved initial conditions for the forecast model. Forecasts were issued based on these improved initial conditions at two nested domains of 6 km and 2 km spatial resolution, with the 2 km domain enclosing Greece. Denial experiments, where no observational data were assimilated in the initial boundary conditions, showed that the temperature fields benefited throughout the forecasting horizon from the assimilation (ranging from a 5 to 10% reduction in the average MAE values), while neutral to slightly positive (ranging from a 0.4 to 2% reduction in the average MAE values) improvement was found for wind, although not throughout the forecast horizon. The increase in spatial resolution did not significantly reduce the forecast error, but was kept at the same small order of magnitude. A tendency of the model to overpredict precipitation regardless of assimilation was observed. The assimilation of the IMERG data improved the precipitation forecasting ability up to the 18th hour of forecast. When compared to assimilation experiments that excluded IMERG data, the assimilation of IMERG data produced a better representation of the spatial distribution of the precipitation fields.

Adjoint-based uncertainty quantification for inhomogeneous friction on a slow-slipping fault

SUMMARY Long-term slow-slip events (LSSEs) usually occur on a fault existing at the deep, shallow parts of subducting plates and substantially relate to adjacent megathrust fault motions. The dynamics of the LSSE largely depend on the inhomogeneity of friction that occurs between the fault interfaces. Thus, it is crucial to estimate the spatial-dependent frictional features from the observations of the slip motion and subsequently identify essential parts that contribute to the principal slip motion by quantifying uncertainties involved in the estimates. Although quantifying the uncertainties of the frictional feature fields in high resolution is necessary to solve the task, conventional techniques of quantifying slow earthquake frictional features have not yet achieved such uncertainty quantification (UQ) due to the complexity of LSSE models such as the large dimensionality. We, therefore, propose a method of UQ for spatially inhomogeneous frictional features from slip motion based on a 4-D variational data assimilation technique using a second-order adjoint method. The proposed method enables us to conduct an accurate UQ even when the dimensionality is large. By combining a fault motion model that mimics slow-slip motion on an LSSE fault–megathrust fault complex in southwestern Japan and the data assimilation technique, we successfully quantified the spatial distribution of the uncertainty of the frictional features in high-resolution. The evaluated spatial distribution in high-resolution reveals the correlation between the dynamics of the slow-slip motion and the important components of the frictional features, which is a valuable information for designing observation systems. Findings from this study are expected to advance the theoretical foundation of applied seismic motion prediction techniques using slow-slip frictional features as stress metres for megaquakes, as well as to improve the understanding of the relationship between the slow-slip motion and frictional parameters of a fault.

Variational data assimilation for a sea dynamics model

Abstract The 4D variational data assimilation technique is presented for modelling the sea dynamics problems, developed at the Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences (INM RAS). The approach is based on the splitting method for the mathematical model of sea dynamics and the minimization of cost functionals related to the observation data by solving an optimality system that involves the adjoint equations and observation and background error covariances. Efficient algorithms for solving the variational data assimilation problems are presented based on iterative processes with a special choice of iterative parameters. The technique is illustrated for the Black Sea dynamics model with variational data assimilation to restore the sea surface heat fluxes.

4D Technology of Variational Data Assimilation for Sea Dynamics Problems

The technology aimed at high-performance computing is presented for modeling the sea dynamics problems based on 4D variational data assimilation technique developed at the Marchuk Institute of Numerical Mathematics, Russian Academy of Sciences (INM RAS). The technology is based on the multicomponent splitting method for the mathematical model of sea dynamics and the minimization of cost functionals related to the observation data by solving an optimality system that involves the adjoint equations with observation data and observation error covariances. Efficient algorithms for solving the variational data assimilation problems are presented based on modern iterative processes with a special choice of iterative parameters. The technology is illustrated for the Baltic Sea dynamics model with variational data assimilation to restore the initial states and the heat fluxes on the sea surface.

Estimation of Root Zone Soil Moisture Profile by Reduced-Order Variational Data Assimilation Using Near Surface Soil Moisture Observations

Soil moisture plays an important role in the global water cycle and has an important impact on energy fluxes at the land surface. It also defines the initial and boundary condition of terrestrial hydrological processes, including infiltration, runoff, and evapotranspiration. Therefore, accurate estimation of soil moisture pattern is of critical importance. Satellite-based soil moisture can be obtained with well-defined temporal and spatial resolutions and with global coverage. However, they only provide surface soil moisture at the upper few centimeters of the soil column. Soil moisture simulation models can produce estimates of soil moisture profile up to several meters of depth in different time steps. However, uncertainty in model parameters (e.g., unknown initial soil moisture profile) and meteorological forcing can substantially alter the accuracy of the model estimates. In this article, the potential of using surface soil moisture measurements to retrieve the initial soil moisture profile will be explored in a synthetic study, using two proposed reduced-order variational data assimilation (VDA) techniques and a simple 1-D soil moisture model. The accuracy and feasibility of the proposed approaches are confirmed by comparing the initial soil moisture profiles estimated using the proposed reduced-order VDA techniques versus the full-adjoint VDA technique. Results illustrated that the reduced-order VDA techniques can estimate initial soil moisture profile from near surface soil moisture observations with the comparable level of accuracy as full-adjoint VDA. The effectiveness of the reduced-order VDA in retrieving the initial soil moisture profile is further demonstrated by assimilating surface soil moisture into HYDRUS-1D, mimicking real-world errors.

A new inverse modeling approach for emission sources based on the DDM-3D and 3DVAR techniques: an application to air quality forecasts in the Beijing–Tianjin–Hebei region

Abstract. We develop a new inversion method which is suitable for linear and nonlinear emission source (ES) modeling, based on the three-dimensional decoupled direct (DDM-3D) sensitivity analysis module in the Community Multiscale Air Quality (CMAQ) model and the three-dimensional variational (3DVAR) data assimilation technique. We established the explicit observation operator matrix between the ES and receptor concentrations and the background error covariance (BEC) matrix of the ES, which can reflect the impacts of uncertainties of the ES on assimilation. Then we constructed the inversion model of the ES by combining the sensitivity analysis with 3DVAR techniques. We performed the simulation experiment using the inversion model for a heavy haze case study in the Beijing–Tianjin–Hebei (BTH) region during 27–30 December 2016. Results show that the spatial distribution of sensitivities of SO2 and NOx ESs to their concentrations, as well as the BEC matrix of ES, is reasonable. Using an a posteriori inversed ES, underestimations of SO2 and NO2 during the heavy haze period are remarkably improved, especially for NO2. Spatial distributions of SO2 and NO2 concentrations simulated by the constrained ES were more accurate compared with an a priori ES in the BTH region. The temporal variations in regionally averaged SO2, NO2, and O3 modeled concentrations using an a posteriori inversed ES are consistent with in situ observations at 45 stations over the BTH region, and simulation errors decrease significantly. These results are of great significance for studies on the formation mechanism of heavy haze, the reduction of uncertainties of the ES and its dynamic updating, and the provision of accurate “virtual” emission inventories for air-quality forecasts and decision-making services for optimization control of air pollution.

Mesoscale Prediction in the Antarctic Using Cycled Ensemble Data Assimilation

AbstractDue in part to sparse conventional observation coverage in the Antarctic region, atmospheric studies in this part of the globe often rely more heavily on numerical models. Model representation of atmospheric processes in the Antarctic remains inferior to representation in the Northern Hemisphere midlatitudes. Poor representation may be related to inaccurate model analyses that do not optimally utilize the limited observation network. Here, the ensemble Kalman filter (EnKF) data assimilation (DA) technique is employed in lieu of variational DA techniques to investigate impacts on model analysis accuracy. This DA technique [provided by the Data Assimilation Research Testbed (DART)] is coupled with a polar-modified, mesoscale numerical model that together compose Antarctic-DART (A-DART). A-DART is cycled with DA and run over a 1-month period, assimilating only conventional observations. Results show relatively good agreement between A-DART and observations. Comparison with radiosonde temperature and geostationary satellite wind observations shows large differences between RMSE and ensemble spread in the upper troposphere. The analysis increment shows large values in the eastern Atlantic–western Indian Oceans associated with geostationary satellite wind observations. Further evaluation determines that geostationary satellite wind observations may be biased in this region. Overall, this baseline demonstration of ensemble-based modeling applied in the Antarctic produced short-term forecasts that were competitive with two operational modeling systems while assimilating on the O(106) fewer observations. A-DART is capable of assimilating additional observations for a variety of applications. This study highlights the capability of applying this ensemble-based DA technique for process and forecast studies in an observation-sparse region.

Modeling Present and Future Permafrost Distribution at the Seward Peninsula, Alaska

AbstractPermafrost, a key component of Arctic ecosystems, is currently affected by climate warming and anticipated to undergo further significant changes in this century. The most pronounced changes are expected to occur in the transition zone between the discontinuous and continuous types of permafrost. We apply a transient temperature dynamic model to investigate the spatiotemporal evolution of permafrost conditions on the Seward Peninsula, Alaska—a region currently characterized by continuous permafrost in its northern part and discontinuous permafrost in the south. We calibrate model parameters using a variational data assimilation technique exploiting historical ground temperature measurements collected across the study area. The model is then evaluated with a separate control set of the ground temperature data. Calibrated model parameters are distributed across the domain according to ecosystem types. The forcing applied to our model consists of historic monthly temperature and precipitation data and climate projections based on the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. Simulated near‐surface permafrost extent for the 2000–2010 decade agrees well with existing permafrost maps and previous Alaska‐wide modeling studies. Future projections suggest a significant increase (3.0°C under RCP 4.5 and 4.4°C under RCP 8.5 at the 2 m depth) in mean decadal ground temperature on average for the peninsula for the 2090–2100 decade when compared to the period of 2000–2010. Widespread degradation of the near‐surface permafrost is projected to reduce its extent at the end of the 21st century to only 43% of the peninsula's area under RCP 4.5 and 8% under RCP 8.5.

LIDA: A Land Integrated Data Assimilation Framework for Mapping Land Surface Heat and Evaporative Fluxes by Assimilating Space‐Borne Soil Moisture and Land Surface Temperature

AbstractLand surface heat and evaporative fluxes exchanged between the land and atmosphere play a crucial role in the terrestrial water and energy balance. Regional mapping of these fluxes is hampered by the lack of in situ measurements (with the required coverage and duration) and the high spatial heterogeneity. In this paper, we propose a Land Integrated Data Assimilation framework (LIDA) based on the variational data assimilation technique to estimate the key parameters of surface heat and evaporative fluxes by jointly assimilating Soil Moisture Active Passive (SMAP) data and Geostationary Operational Environmental Satellite (GOES) surface temperature data into a coupled parsimonious land water and energy balance model. The method is implemented over an area of 31,500 km2 in the U.S. Southern Great Plains, and its performance is evaluated through consistency tests, comparison tests, and uncertainty analyses. The maps of retrieved heat and evaporative fluxes are used to analyze a range of feedback mechanisms in land‐atmosphere interaction, such as the dependence of evapotranspiration on vegetation and water availability.

Numerical Modeling of Marine Circulation with 4D Variational Data Assimilation

The technology is presented for modeling and prediction of marine hydrophysical fields based on the 4D variational data assimilation technique developed at the Marchuk Institute of Numerical Mathematics, Russian Academy of Sciences (INM RAS). The technology is based on solving equations of marine hydrodynamics using multicomponent splitting, thereby solving an optimality system that includes adjoint equations and covariance matrices of observation errors. The hydrodynamic model is described by primitive equations in the sigma-coordinate system, which is solved by finite-difference methods. The technology includes original algorithms for solving the problems of variational data assimilation using modern iterative processes with a special choice of iterative parameters. The methods and technology are illustrated by the example of solving the problem of circulation of the Baltic Sea with 4D variational data assimilation of sea surface temperature information.

Improving air quality forecasting with the assimilation of GOCI aerosol optical depth (AOD) retrievals during the KORUS-AQ period

Abstract. The Korean Geostationary Ocean Color Imager (GOCI) satellite has monitored the East Asian region in high temporal (e.g., hourly) and spatial resolution (e.g., 6 km) every day for the last decade, providing unprecedented information on air pollutants over the upstream region of the Korean Peninsula. In this study, the GOCI aerosol optical depth (AOD), retrieved at the 550 nm wavelength, is assimilated to enhance the quality of the aerosol analysis, thereby making systematic improvements to air quality forecasting over South Korea. For successful data assimilation, GOCI retrievals are carefully investigated and processed based on data characteristics such as temporal and spatial distribution. The preprocessed data are then assimilated in the three-dimensional variational data assimilation (3D-Var) technique for the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). For the Korea–United States Air Quality (KORUS-AQ) period (May 2016), the impact of GOCI AOD on the accuracy of surface PM2.5 prediction is examined by comparing with effects of other observations including Moderate Resolution Imaging Spectroradiometer (MODIS) sensors and surface PM2.5 observations. Consistent with previous studies, the assimilation of surface PM2.5 measurements alone still underestimates surface PM2.5 concentrations in the following forecasts, and the forecast improvements last only for about 6 h. When GOCI AOD retrievals are assimilated with surface PM2.5 observations, however, the negative bias is diminished and forecast skills are improved up to 24 h, with the most significant contributions to the prediction of heavy pollution events over South Korea.

X-Net-Based Radar Data Assimilation Study over the Seoul Metropolitan Area

This study investigates the ability of the high-resolution Weather Research and Forecasting (WRF) model to simulate summer precipitation with assimilation of X-band radar network data (X-Net) over the Seoul metropolitan area. Numerical data assimilation (DA) experiments with X-Net (S- and X-band Doppler radar) radial velocity and reflectivity data for three events of convective systems along the Changma front are conducted. In addition to the conventional assimilation of radar data, which focuses on assimilating the radial velocity and reflectivity of precipitation echoes, this study assimilates null-echoes and analyzes the effect of null-echo data assimilation on short-term quantitative precipitation forecasting (QPF). A null-echo is defined as a region with non-precipitation echoes within the radar observation range. The model removes excessive humidity and four types of hydrometeors (wet and dry snow, graupel, and rain) based on the radar reflectivity by using a three-dimensional variational (3D-Var) data assimilation technique within the WRFDA system. Some procedures for preprocessing radar reflectivity data and using null-echoes in this assimilation are discussed. Numerical experiments with conventional radar DA over-predicted the precipitation. However, experiments with additional null-echo information removed excessive water vapor and hydrometeors and suppressed erroneous model precipitation. The results of statistical model verification showed improvements in the analysis and objective forecast scores, reducing the amount of over-predicted precipitation. An analysis of a contoured frequency by altitude diagram (CFAD) and time–height cross-sections showed that increased hydrometeors throughout the data assimilation period enhanced precipitation formation, and reflectivity under the melting layer was simulated similarly to the observations during the peak precipitation times. In addition, overestimated hydrometeors were reduced through null-echo data assimilation.

Appraisal of Data Assimilation Techniques for Dynamical Downscaling of the Structure and Intensity of Tropical Cyclones

AbstractThe dynamical downscaling technique is used for the understanding of physical mechanisms associated with the atmospheric phenomena. We have developed high‐resolution analysis (6 km) for three tropical cyclones (TCs), namely, Phailin (2013), Nilofar (2014), and Chapala (2015) originated over the North Indian Ocean using the dynamical downscaling approach. The study aimed at the identification of appropriate methodology for generating analysis so that it becomes useful for identifying the role of environmental and internal dynamics on intensification processes and structural changes of TCs. The simulations using Weather Research and Forecasting model and four‐dimensional variational (4DVAR), hybrid three‐dimensional ensemble‐variational (3DEnVAR), and a hybrid four‐dimensional ensemble‐variational (4DEnVAR) data assimilation (DA) techniques are compared. The impact of DA is quantified by comparing errors in position, minimum sea level pressure, and maximum wind speed with the best track data set of India Meteorological Department. The intensities of TCs simulated by three downscaling methods are validated in terms of changes in minimum sea level pressure, maximum surface winds, and boundary layer and middle tropospheric relative humidity. The skills scores, namely, equitable threat score, false alarm ratio, the probability of detection, and biases (BIAS), are calculated to identify the best suitable DA technique. It is found that the hybrid DA techniques improve the overall quality of analysis compared to those developed using only variational DA techniques. The simulation using the hybrid 4DEnVAR DA technique is found to be better for simulation of the track, intensity changes, and structural characteristics of TCs.