Single Observation Experiments Research Articles

Simultaneous Multiscale 4DEnVar With Scale Dependent Localization (SDL) in Hurricane Analysis and Forecast System (HAFS)

AbstractThis study presents the implementation and evaluation of scale‐dependent localization (SDL) in the hurricane analysis and forecast system 4D Ensemble Variational (4DEnVar) Data Assimilation (DA) system. The SDL capability is compared with the traditional Single‐Scale Localization (SSL) method to assess its benefits and necessity for hurricane prediction. The experiments focus on Hurricane Laura (2020) and involve single observation experiments as well as real observation DA cycling experiments. The results indicate that the SDL experiment, which incorporates the Fast Almost Gaussian Filtering approach for scale decomposition, consistently outperforms the corresponding SSL configurations in almost all aspects. Further diagnostics show that due to its multiscale nature, the SDL approach demonstrates better track prediction over small‐scale SSL due to improved environmental analysis and better analyzed vortex position and structure, and superior intensity prediction during the rapid intensification over both the large‐scale SSL and the small‐scale SSL owing to enhanced inner‐core thermodynamic analysis.

Comparison of different momentum control variables on assimilating radar observations for the forecasts of a dispersive convective event

In this study, the effects of background error covariance (BE) using the stream function ψ and unbalanced velocity potential χu as momentum control variables (CV5 scheme) and BE using the velocity U and V as momentum control variables (CV7 scheme) on assimilating radar radial velocity and reflectivity data for short-term forecasts of dispersive convection in a weak environmental field are explored based on the weather research and forecasting model (WRF) model and its 3DVAR assimilation system. The 4 km resolution forecast samples are generated to formulate the CV5 and CV7 BE by the National Meteorological Center (NMC) method. The single-observation experiments reveal that the differences between the two BE statistics are mainly reflected on the momentum control variables. The increment of wind field from CV7 shows more small-scale local characteristics. Comparing with control experiment, real radar observation assimilation tests of CV5 and CV7 both improve the reflectivity and precipitation forecasts. But the CV7 scheme improves the forecasting of strong convective systems in weak environmental fields better than CV5. First, the CV7 scheme improves both reflectivity and dispersive precipitation forecasts and significantly suppresses the spurious precipitation forecasts when compared with the CV5 scheme. In addition, CV7 also significantly reduces the forecast errors of surface variables and the wind analysis from CV7 is more local. Further analysis shows that the CV7 improves the water vapor convergence conditions compared to the CV5 scheme, which may be the reason for its better performance in the subsequent forecasts.

Application of Radar Radial Velocity Data Assimilation Based on Different Momentum Control Variables in Forecasting Typhoon Kompasu

The study is based on the Weather Research and Forecasting Model (WRF) and the three-dimensional variational (3DVAR) data assimilation system. Based on two different control variables, the effects of radar radial velocity assimilation in forecasting of the tropical cyclone (TC) Kompasu were evaluated. The single observation experiment showed that DA_ψχ produces cyclonic increments, while DA_UV only produces increments in the same direction as the observation. DA_ψχ significantly enhances the wind field at 850 hPa with a large number of unphysical cyclonic increments. On the other hand, DA_UV produces reasonable cyclonic increments to enhance the TC. The assimilation of DA_UV makes the surface wind enhanced and the sea level pressure at the TC center reduced. The circular structure of the DA_ψχ wind field is not clear and neither is the large wind area concentrated. In addition, the DA_ψχ shows spurious convection at the high altitude of the vertical cross section, while the DA_UV presents enhanced large wind area at the bottom. The RMSE of the radial velocity is smaller during the circular assimilation in DA_UV. DA_ψχ does not improve the track forecast of Kompasu, while DA_UV shows a significant improvement by the track forecast.

Effect of Flow-Dependent Unbalanced Background Error Variances on Tropical Cyclone Forecasting

The background error variance in variational data assimilation can significantly affect a model’s initial field. Around extreme weather events, the variance of the unbalanced control variables have contributed highly to the total variance. This study investigates the effect of flow-dependent unbalanced variance on tropical cyclone (TC) forecasts using the ensemble of data assimilation (EDA) method. The analysis of TC Saudel (October 2020) shows that flow-dependent unbalanced variances can better represent the uncertainty in the background error, which is investigated in terms of magnitude and distribution. The vertical distribution of the temperature-explained variance ratio also shows that the contribution of the vorticity-balanced variance around Saudel is lower than the global average (in the troposphere). Single-observation experiments reveal that the structured flow-dependent errors of unbalanced control variables can also introduce corresponding structural information in analysis increments. As expected, the experiments in which the variances of all variables are flow-dependent in the one-month TC forecast performed better overall. Compared with the reference, these forecasts reduce the average absolute track and intensity errors by approximately 31% and 9%, respectively. The results demonstrate that EDA-based unbalanced variances can indeed improve the mean forecast skills of TC tracks and intensities despite instability at some lead times by improving the forecast of the circulation situation and providing a more appropriate balance relationship between variables.

A preliminary evaluation of FY-4A visible radiance data assimilation by the WRF (ARW v4.1.1)/DART (Manhattan release v9.8.0)-RTTOV (v12.3) system for a tropical storm case

Abstract. Satellite visible radiance data that contain rich cloud and precipitation information are increasingly assimilated to improve the forecasts of numerical weather prediction models. This study evaluates the Data Assimilation Research Testbed (DART, Manhattan release v9.8.0), coupled with the Weather Research and Forecasting (WRF) model (ARW v4.1.1) and the Radiative Transfer for TOVS (RTTOV, v12.3) package, for assimilating the simulated visible imagery of the FY-4A geostationary satellite located over Asia in an Observing System Simulation Experiment (OSSE) framework. The OSSE was performed for the tropical storm Higos that occurred in 2020 and contains multi-layer mixed-phase cloud and precipitation processes. The advantages and limitations of DART for assimilating FY-4A visible imagery were evaluated. Both single-observation experiments and cycled data assimilation (DA) experiments were performed to study the impact of different filter algorithms available in DART, variables being cycled, observation outlier thresholds, observation errors, and observation thinning. The results show that assimilating visible radiance data significantly improves the analysis of the cloud water path (CWP) and cloud coverage (CFC) from first-guess forecasts. The rank histogram filter (RHF) allows WRF to more accurately simulate CWP and CFC compared with the ensemble adjustment Kalman filter (EAKF) although it took roughly twice as long as the latter. By cycling both cloud and non-cloud variables, specifying large outlier threshold values, or setting smaller observation errors without thinning of observations, WRF achieved a better simulation of CWP and CFC. With model integration, DA of the visible radiance data also generated a slightly positive impact on non-cloud variables as they were adjusted through the model dynamics and physics related to cloud processes. In addition, the DA improved the representation of precipitation. However, the impact on the rain rate is limited by the inabilities of the DA to improve cloud vertical structures and cloud phases. A negative impact of the DA on cloud variables was found due to the nature of the non-linear forward operator and the non-Gaussian distribution of the prior. Future works should explore faster and more accurate forward operators suitable for assimilating FY-4A visible imagery, techniques to reduce the non-linear and non-Gaussian errors, and methods to correct the location errors which correspond to the clouds underestimated by the first guess.

Preliminary evaluation of all-sky radiance assimilation scheme with POD-3DEnVar method

To meet the challenges in satellite data assimilation involving cloud and precipitation processes, an all-sky radiance assimilation scheme (referred to as the ‘scheme’) that includes hydrometeor analysis is constructed based on the Proper Orthogonal Decomposition (POD)-3DEnVar method. Three key elements of the scheme are: (1) four hydrometeor mixing ratios for cloud-water, cloud-ice, rain, and snow in the control variables of the POD-3DEnVar method; (2) an extension of the Community Radiative Transfer Model interface to include hydrometeor profiles; and (3) multiple physical process model forecasts that use multiple microphysical and cumulus parameterization schemes as ensemble samples to estimate the flow-dependent background error covariance of the control variables, including those for hydrometeors. To evaluate the performance of the scheme, single-observation experiments and observation system simulation experiments (OSSEs) were designed for a binary typhoon case. Results for the single observation experiments show that the scheme can accurately assimilate satellite radiance near typhoon centers and produce reasonable increments for the hydrometeor variables. The background error covariances of the hydrometeor variables have reasonable flow-dependent characteristics. Results for the OSSEs have root-mean-square errors for all control variables of the assimilation analysis field with respect to the ‘true’ field that are lower than those obtained without assimilation. This indicates that the scheme effectively reduces error for all control variables, including those for hydrometeors. Forecasting experiments reveal that these improvements in the assimilation of control variables result in improved forecasts. However, improvements in the hydrometeor variables are of shorter duration than are those of the other control variables. The impact of improvements in the assimilation on typhoon track and intensity are obvious in the forecasting field. • An all-sky radiance assimilation scheme is constructed based on the POD-3DEnVar method. • The OSSEs indicate that the scheme reduces error for all control variables, including those for hydrometeors. • The impact of improvements in the assimilation on typhoon track and intensity are obvious in the forecasting field.

Effects of Flow Dependency Introduced by Background Error in Frequent and Dense Assimilation of Radial Winds Using Observation Error Correlated in Time and Space

Abstract We investigated the effect of flow dependency in the assimilation of high-density, high-frequency observations. Radial winds from a Doppler radar are assimilated using a regional hybrid four-dimensional variational data assimilation (4D-Var) scheme with a flow-dependent background error covariance. To consistently assimilate 5 km × 5.625° cell-averaged radial winds at an interval of 10 min, the spatial and temporal correlations of the observation error are statistically diagnosed to be incorporated into the hybrid 4D-Var. The spatial correlation width is larger than that expected from instrument error, suggesting a contribution from representation error whose propagation is also considered to lead to temporal correlation, the width of which is diagnosed to increase with forecast time. The background error covariance also has an important role in incorporating observational information into the analysis. Single observation experiments show that the hybrid 4D-Var has more small-scale structure in its flow-dependent background error correlation than the 4D-Var limited from the climatological background error covariance mainly in the former part of the assimilation window. This suggests the higher potential of the hybrid 4D-Var to allow more higher-wavenumber components in the increment. A case study shows that the hybrid 4D-Var makes better use of the dense and frequent observations, reflecting more detailed representation of flow throughout the assimilation window, leading to promising results in the forecast. Sensitivity experiments also show that it is important to use the optimal observation error correlation. It is suggested that the flow-dependent background error becomes necessary to effectively use high-resolution, high-frequency observations.

The Influence of Aerosols on Satellite Infrared Radiance Simulations and Jacobians: Numerical Experiments of CRTM and GSI

For a variational data assimilation (DA) system that assimilates radiance observations, the simulated brightness temperature (BT) at the top of the atmosphere and the corresponding Jacobians carried out by the radiance observation operator are needed information. Previous studies reported that the incorporation of aerosol information into the radiance observation operator leads to cooler simulated infrared (IR) BTs and warmer temperature analyses at low levels of the atmosphere. However, the role of the aerosol-affected Jacobians in the DA system, which not only affect the determination of analysis increments but also influence the quality control and the bias correction algorithm, is yet to be investigated. This study examines the aerosol impacts on the sensitivity of IR radiance simulations, Jacobians, and the analysis increments by conducting two experiments: (i) sensitivity tests to assess how the different aspects of the aerosol profiles (i.e., mass loading, peak aerosol level, aerosol thickness layer, and bin partition) affect the simulated BT and the Jacobians from the Community Radiative Transfer Model (CRTM), which is the radiance observation operator in the Gridpoint Statistical Interpolation (GSI) analysis system; (ii) single IR observation experiments using GSI to investigate how the aerosol-affected atmospheric Jacobians influence the analysis increment. The results show that dust aerosols produce the strongest cooling to simulated BTs under similar aerosol optical depths; simulated BTs and Jacobians are most sensitive to the loading and peak altitude of the aerosol layer; simulated BTs become more sensitive to the temperature of the aerosol layer; aerosol-induced differences in atmospheric Jacobians lead to considerable changes to temperature and moisture increments. These results provide a better understanding of the aerosol impacts on each component involved in radiance DA, which can provide guidance for assimilating aerosol-affected IR observations.

Cloud-dependent piecewise assimilation based on a hydrometeor-included background error covariance and its impact on regional Numerical Weather Prediction

AbstractThe background error covariance (B) behaves differently and needs to be carefully defined in cloudy areas due to larger uncertainties caused by models’ inability to correctly represent complex physical processes. This study proposes a new cloud-dependent B strategy by adaptively adjusting the hydrometeor-included B in the cloudy areas according to the cloud index (CI) derived from the satellite-based cloud products. The adjustment coefficient is determined by comparing the error statistics of B for the clear and cloudy areas based on the two-dimensional geographical masks. The comparison highlights the larger forecast errors and manifests the necessity of using appropriate B in cloudy areas. The cloud-dependent B is then evaluated by a series of single observation tests and three-week cycling assimilation and forecasting experiments. The single observation experiments confirm that the cloud-dependent B allows cloud dependency for the multivariate analysis increments and alleviates the discontinuities at the cloud mask borders by treating the CI as an exponent. The impact study on regional numerical weather prediction (NWP) demonstrates that the application of the cloud-dependent B reduces analyses and forecasts bias and increases precipitation forecast skills. Diagnostics of a heavy rainfall case indicate that the application of the cloud-dependent B enhances the moisture, wind, and hydrometeors analyses and forecasts, resulting in more accurate forecasts of accumulated precipitation. The cloud-dependent piecewise analysis scheme proposed herein is extensible, and a more precise definition of CI can improve the analysis, which deserves future investigation.

A comparison of variational, ensemble-based, and hybrid data assimilation methods over East Asia for two one-month periods

In this study, the performances of variational (three-dimensional variational; 3DVAR), ensemble-based (ensemble Kalman filter; EnKF), and hybrid (E3DVAR) data assimilation (DA) methods based on the Advanced Research Weather Research and Forecasting (WRF) model are investigated over East Asia for two one-month period of January and July in 2016. Before a comparison between three methods for two one-month periods, a single observation experiment is conducted to tune and optimize background error covariance depending on each method, so that all methods have similar influence radius. For a comparison between three methods for two one-month period by assimilating conventional observations, the E3DVAR outperforms 3DVAR and EnKF for both two seasons. The 3DVAR outperforms EnKF in January, whereas EnKF outperforms 3DVAR in July. The root mean of difference total energy (RM-DTE) for January increases as a forecast time increases, saturating at the value less than 5 m s −1 . On the contrary, RM-DTE in July keeps increasing until 72 h forecast time reaching at the value less than 7 m s −1 . Relatively larger moisture error in initial condition for summer season can grow rapidly and change large-scale feature considerably, which can contribute to the continuous growth of RM-DTE in July. Furthermore, rank histogram and spread statistics results confirm that ensemble spreads are represented reasonably for January and July in 2016, although spreads in July are slightly overestimated compared to those in January. In conclusion, the hybrid DA method (E3DVAR) is the most appropriate among three DA methods over East Asia. In addition, for the better performance, it is necessary to tune and optimize the DA system depending on DA method for the given area. • The performances of 3DVAR, EnKF, and E3DVAR based on WRF are investigated over East Asia for January and July in 2016. • The E3DVAR outperforms 3DVAR and EnKF over East Asia for January and July in 2016. • The hybrid data assimilation method as E3DVAR is the most appropriate among three methods over East Asia.

A variational successive corrections approach for the sea ice concentration analysis

The sea ice concentration observation from satellite remote sensing includes the spatial multi-scale information. However, traditional data assimilation methods cannot better extract the valuable information due to the complicated variability of the sea ice concentration in the marginal ice zone. A successive corrections analysis using variational optimization method, called spatial multi-scale recursive filter (SMRF), has been designed in this paper to extract multi-scale information resolved by sea ice observations. It is a combination of successive correction methods (SCM) and minimization algorithms, in which various observational scales, from longer to shorter wavelengths, can be extracted successively. As a variational objective analysis scheme, it gains the advantage over the conventional approaches that analyze all scales resolved by observations at one time, and also, the specification of parameters is more convenient. Results of single-observation experiment demonstrate that the SMRF scheme possesses a good ability in propagating observational signals. Further, it shows a superior performance in extracting multi-scale information in a two-dimensional sea ice concentration (SIC) experiment with the real observations from Special Sensor Microwave/Imager SIC (SSMI).

The Framework for Assimilating All-Sky GPM Microwave Imager Brightness Temperature Data in the NASA GEOS Data Assimilation System

Abstract Satellite radiance observations combine global coverage with high temporal and spatial resolution, and bring vital information to NWP analyses especially in areas where conventional data are sparse. However, most satellite observations that are actively assimilated have been limited to clear-sky conditions due to difficulties associated with accounting for non-Gaussian error characteristics, nonlinearity, and the development of appropriate observation operators for cloud- and precipitation-affected satellite radiance data. This article provides an overview of the development of the Gridpoint Statistical Interpolation (GSI) configurations to assimilate all-sky data from microwave imagers such as the GPM Microwave Imager (GMI) in the NASA Goddard Earth Observing System (GEOS). Electromagnetic characteristics associated with their wavelengths allow microwave imager data to be highly sensitive to precipitation. Therefore, all-sky data assimilation efforts described in this study are primarily focused on utilizing these data in precipitating regions. To utilize data in cloudy and precipitating regions, state and analysis variables have been added for ice cloud, liquid cloud, rain, and snow. This required enhancing the observation operator to simulate radiances in heavy precipitation, including frozen precipitation. Background error covariances in both the central analysis and EnKF analysis in the GEOS hybrid 4D-EnVar system have been expanded to include hydrometeors. In addition, the bias correction scheme was enhanced to reduce biases associated with thick clouds and precipitation. The results from single observation experiments demonstrate the capability of assimilating all-sky microwave brightness temperature data in GEOS both when the model forecast produces excessive precipitation and too little precipitation. Additional experiments show that hydrometeors and dynamic variables such as winds and pressure are adjusted in physically consistent ways in response to the assimilation.

Research on the Plasma Channel Expansion Behavior of Cathode in Electrical Arc Machining

Abstract As a thermal erosion process, the performance of electrical arc machining (EAM)—including material removal rate (MRR) and tool wear ratio (TWR)—is greatly influenced by arc plasma in the gap between the workpiece and the electrode. Therefore, it is necessary to study plasma channel expansion behaviour (PCEB) to disclose the EAM mechanism. By far, negative polarity EAM is usually adopted to achieve a higher MRR; most current research concentrates on the PCEB of the anode side for it directly relates to the MRR. However, in electrical machining, the plasma ignites from the cathode side, and positive polarity EAM is also recommended to improve the surface quality. Thus, the PCEB of the cathode is also very important. In this paper, a set of single discharge observation experiments was implemented to investigate the PCEB at cathode side by utilizing a four-frame high-speed camera. Based on time solved imaging of single discharge, the relationship between the PCEB and primary electrical parameters, including discharge time and peak current, was studied. Besides, the PCEBs of three conventional electrode materials were compared to find the mutual relationship between the material properties and expansion behaviour. The PCEB formulations of these materials under the specific parameters were further deduced and applied to simulate the single discharge crater. Finally, to elaborate the validity of the formulations, the simulation results above were compared with the results obtained by using uniform plasma diameter.

Assimilation of INSAT-3D imager water vapour clear sky brightness temperature in the NCMRWF’s assimilation and forecast system

This paper describes the direct assimilation of water vapour (WV) clear sky brightness temperatures (CSBTs) from the INSAT-3D imager in the National Centre for Medium Range Weather Forecasting (NCMRWF) Unified Model (NCUM) assimilation and forecast system. INSAT-3D imager WV CSBTs show a systematic bias of 2–3 K compared to the data simulated from the model first guess fields in the pre-assimilation study. The bias in the INSAT-3D imager WV CSBTs is removed using a statistical bias correction prior to assimilation. The impact of INSAT-3D imager WV channel CSBTs is investigated through different approaches: (i) single observation experiments and (ii) global assimilation experiments using the hybrid-four-dimensional variational technique. Single observation experiments of channels of the same frequency from different instruments like the INSAT-3D imager and sounder, and the Meteosat visible and infrared imager (MVIRI) onboard Meteosat-7, show the INSAT-3D imager and MVIRI WV channels have a similar impact on the analysis increment. Global assimilation clearly shows the positive impact of the INSAT-3D imager WV CSBTs on the humidity and upper tropospheric wind fields, whereas the impact on the temperature field, particularly over the tropics, is neutral. Validation of model forecasted parameters with the in situ radio sonde observations also showed the positive impact of assimilation on the humidity and wind fields. INSAT-3D imager WV CSBTs have been assimilated operationally in NCUM since August 2018.

FY-3A Microwave Data Assimilation Based on the POD-4DEnVar Method

A four-dimensional ensemble variational assimilation system for FY-3A satellite data is constructed using the Proper Orthogonal Decomposition (POD)-based ensemble four-dimensional variational (4DVar) assimilation method (referred to as POD-4DEnVar Satellite Assimilation System). Using the community radiative transfer model (CRTM) as the observation operator for satellite data, ensemble samples are mapped to the observation space and observation perturbations are generated. The observation perturbations matrix of satellite data is then decomposed to obtain the orthogonal eigenvectors and the eigenvalues for the observation perturbations matrix. The observation perturbations matrix and model perturbations matrix are transformed using orthogonal eigenvectors as basis functions and an explicit expression for the analysis increment is obtained. The expression includes the flow-dependent background error covariance and avoids the difficulty of solving the adjoint model for four-dimensional variational assimilation. In order to evaluate the capability of POD-4DEnVar Satellite Assimilation System, single observation experiments and observation system simulation experiments (OSSEs) for FY-3A MWHS and MWTS sensor data were designed to simulate a large-scale precipitation event occurring over the middle and lower reaches of the Yangtze River. The results of single observation experiments show that POD-4DEnVar Satellite Assimilation System can assimilate satellite data correctly, and the background error covariance of POD-4DEnVar Satellite Assimilation System has obvious flow-dependent characteristics. The results of the OSSEs show that the root-mean-square errors (RMSEs) of the assimilation analysis field with respect to the “true” field are lower than those of the background field, which indicates that the POD-4DEnVar Satellite Assimilation System can assimilate satellite data effectively. The sensitivity of the POD-4DEnVar Satellite Assimilation System to the percentage of truncated eigenvalues, the number of ensemble members, assimilation time window length, and the horizontal localization scale (which are key parameters for POD-4DEnVar Satellite Assimilation System) was tested in sensitivity experiments. These experiments show that if the percentage of truncated eigenvalues for POD decomposition is more than 80%, POD-4DEnVar Satellite Assimilation System has strong assimilation skill. Increasing the number of initial ensemble members has little influence on the assimilation ability of POD-4DEnVar Satellite Assimilation System. But, increasing the number of the physical ensemble members can clearly increase the assimilation ability. The assimilation skill of POD-4DEnVar Satellite Assimilation System is optimal when the length of the assimilation time window is 5 h or 3 h and the horizontal localization scale is 500 km or above. The assimilation ability of POD-4DEnVar Satellite Assimilation System is preliminarily tested by single observation experiments and OSSEs. The results show that it is feasible to assimilate satellite data using the POD-4DEnVar method. In the future, a variety of real satellite data and a variety of mesoscale weather cases will be used to further verify the stability of POD-4DEnVar Satellite Assimilation System.

A case study involving single observation experiments performed over snowy Siberia using a coupled atmosphere‐land modelling system

AbstractThrough a series of single observation experiments, we investigate forecast error covariance and correlation structures by applying the Maximum Likelihood Ensemble Filter (MLEF) data assimilation method with a coupled atmosphere–land surface model. We consider a coupled approach that includes the full complexity of the cross‐component covariance and correlation structures, specifically between 2‐m air temperature and snow temperature. We show that the error covariance and correlation are complex and are strongly dependent on both weather conditions and the land surface scheme, indicating a need for further investigation of the relevance of flow‐dependent ensemble covariance in coupled systems. We also demonstrate that the use of coupled error covariance methods improves the efficiency of information transfer between the atmosphere and the land surface by allowing the well‐observed atmosphere to influence land surface variables.

Precipitation data assimilation in WRFDA 4D-Var: implementation and application to convection-permitting forecasts over United States

Precipitation data assimilation has been developed in the Weather Research and Forecasting model data assimilation system (WRFDA) using four-dimensional variational (4D-Var) approach. Unlike other conventional data, precipitation is an integral quantity and it is not included as a control variable in WRFDA. A simplified Kessler scheme is used in tangent linear and adjoint model. Precipitation data are directly assimilated in WRFDA 4D-Var, and the assimilation of precipitation will have feedback to all the control variables via the constraint of the linearized physics package. Firstly, we present single observation experiments to exhibit how dynamic, thermodynamic and moisture fields are adjusted by assimilating rainfall information. Then, the National Centers for Environmental Prediction Stage IV precipitation data are assimilated for a heavy rainfall case on 9 June 2010 at a convection-permitting model setting (4-km). Finally, we conducted one-week experiments to further validate the robustness of the results for precipitation assimilation. Results show that precipitation assimilation has a positive impact on model fields, particularly on the low-level humidity. For the impact on precipitation forecasts, it indicates that precipitation assimilation reduces spin-up time efficiently, removes false alarms and produces model forecast precipitation closer to the observations through the changes in temperature, moisture and wind imposed in the analyses. The impact from precipitation assimilation persists up to three hours on average.

Reducing ocean model imbalances in the equatorial region caused by data assimilation

The equatorial region is a particularly challenging area for ocean data assimilation. In this region the dominant balance near the surface is between the ocean pressure gradients and the applied wind stress. When increments are applied to an ocean model near the Equator, the pressure gradients are modified and this can cause an imbalance with the unchanged wind stress. This can lead to an initialization shock where spurious equatorial waves and vertical velocities are triggered. The equatorial waves can degrade the performance of the ocean model while the increased vertical velocities have a significant negative impact on biogeochemical models forced by these physical ocean fields. Previous studies have proposed a scheme to reduce the ocean biases associated with slowly varying errors in the applied wind stress by applying a pressure correction calculated from accumulated temperature and salinity increments. This scheme has previously been shown to reduce the mean vertical velocities and improve modelled circulation. However, when applying this scheme, we still see equatorial imbalances from data assimilation on shorter time‐scales.We consider a new method which is an extension of the pressure correction and aims to act as a balance to the equatorial increments and reduce intialization shock. We refer to this as an incremental pressure correction. We provide a description of this method and an analysis of its energetics. In initial experiments with a global ocean data assimilation system, this new method is shown to suppress the generation of equatorial waves in a single observation experiment and to significantly reduce the standard deviation of vertical velocities in a re‐analysis experiment.

Assimilating surface observations in a four-dimensional variational Doppler radar data assimilation system to improve the analysis and forecast of a squall line case

AbstractThis paper examines how assimilating surface observations can improve the analysis and forecast ability of a fourdimensional Variational Doppler Radar Analysis System (VDRAS). Observed surface temperature and winds are assimilated together with radar radial velocity and reflectivity into a convection-permitting model using the VDRAS four-dimensional variational (4DVAR) data assimilation system. A squall-line case observed during a field campaign is selected to investigate the performance of the technique. A single observation experiment shows that assimilating surface observations can influence the analyzed fields in both the horizontal and vertical directions. The surface-based cold pool, divergence and gust front of the squall line are all strengthened through the assimilation of the single surface observation. Three experiments—assimilating radar data only, assimilating radar data with surface data blended in a mesoscale background, and assimilating both radar and surface observations with a 4DVAR cost function—are conducted to examine the impact of the surface data assimilation. Independent surface and wind profiler observations are used for verification. The result shows that the analysis and forecast are improved when surface observations are assimilated in addition to radar observations. It is also shown that the additional surface data can help improve the analysis and forecast at low levels. Surface and low-level features of the squall line—including the surface warm inflow, cold pool, gust front, and low-level wind—are much closer to the observations after assimilating the surface data in VDRAS.

TheGSIcapability to assimilateTRMMandGPMhydrometeor retrievals inHWRF

Hurricane forecasting skills may be improved by utilizing increased precipitation observations available from the Global Precipitation Measurement mission (GPM). This study adds to the Gridpoint Statistical Interpolation (GSI)capability to assimilate satellite‐retrieved hydrometeor profile data in the operational Hurricane Weather Research and Forecasting (HWRF)system. The newly developed Hurricane Goddard Profiling (GPROF)algorithm produces Tropical Rainfall Measuring Mission (TRMM)/GPMhydrometeor retrievals specifically for hurricanes. Two new observation operators are developed and implemented inGSIto assimilate HurricaneGPROFretrieved hydrometeors inHWRF. They are based on the assumption that all water vapour in excess of saturation with respect to ice or liquid is immediately condensed out. Two sets of single observation experiments that include assimilation of solid or liquid hydrometeor from HurricaneGPROFare performed. Results suggest that assimilating single retrieved solid or liquid hydrometeor information impacts the current set of control variables ofGSIby adjusting the environment that includes temperature, pressure and moisture fields toward saturation with respect to ice or liquid. These results are explained in a physically consistent manner, implying satisfactory observation operators and meaningful structure of background error covariance employed byGSI. Applied to two real hurricane cases,Leslie(2012) andGonzalo(2014), the assimilation of the HurricaneGPROFdata in the innermost domain ofHWRFshows a physically reasonable adjustment and an improvement of the analysis compared to observations. However, the impact of assimilating the HurricaneGPROFretrieved hydrometeors on the subsequentHWRFforecasts, measured by hurricane tracks, intensities, sizes, satellite‐retrieved rain rates, and corresponding infrared images, is inconclusive. Possible causes are discussed.