Xiang-Yu Huang | National Center for Atmospheric Research (original) (raw)
Papers by Xiang-Yu Huang
Plateau Meteorology, Apr 24, 2016
Atmosphere
This paper presents a new blending approach to applying large-scale correction to the initial con... more This paper presents a new blending approach to applying large-scale correction to the initial condition in a limited-area numerical weather prediction (NWP) model. The new approach combines the implementation benefits of the known approaches and shows significant improvement in the forecast quality when implemented in a tropical NWP model. Sensitivity studies indicate that many improvements come from blending the horizontal winds alone. Adding temperature and specific humidity to the horizontal winds result in forecast quality degradation in the early hours of the simulated tropical environment.
Quarterly Journal of the Royal Meteorological Society, 2020
The background error covariance matrix plays a vital role in any data assimilation system. Proper... more The background error covariance matrix plays a vital role in any data assimilation system. Proper specification, which is determined by the forecast system setup , is often required. Previous studies have investigated its relevance in various global and regional numerical weather prediction (NWP) systems; however, very few have explored it in tropical NWP systems. Here, we present and evaluate the structures of the background error covariance matrix for a tropical convective-scale NWP system. A total of 12 background error covariance matrices are modelled using differences between pairs of forecasts of different lengths but valid at the same time, based on the application of the vertical-first and horizontal-first transform order formulations on six permutations of the training data. Through pseudo-single observation tests, we extract and test the sensitivity of their structures to the training data period (seasons), forecast lag and transform order. The structures typically exhibit more dependence on forecast lag and transform order; horizontal-first transform order covariances had structures with shorter horizontal length-scales for wind and larger wind background error standard deviations. We also note that some covariances had horizontal and vertical structures with stronger mass-wind coupling, closely resembling an equatorial Kelvin wave. To assess the performance of each of the covariances, 12 month-long data assimilation trials in May 2018 (characterised by frequent occurrences of localised thunderstorm events) are performed. We show improved short-range precipitation forecasts in trials using some of the covariances compared to the current operational covariance. These covariances generally have structures with weak mass-wind coupling, shorter horizontal length-scales for wind and larger wind background error standard deviations, compared to other covariances which led to poorer forecasts. These may be desirable factors when modelling the background error covariance matrix for tropical convective-scale data assimilation systems.
<p>Squall lines are the prominent feature over Singapore region cre... more <p>Squall lines are the prominent feature over Singapore region creating strongly localized rain events due to vigorous localized convective activity. These convective systems have relatively small spatial and temporal scales compared to other atmospheric features like monsoons, thus the prediction of these features lack accuracy. The SINGV numerical weather prediction model is able to provide improved weather forecasts over Singapore region, however, challenges still exist in predicting the thunderstorm/squall line events in onset, location, intensity and lead time. A few real-time case studies of squall lines indicate that SINGV could not capture these features appropriately, while WRF did a better forecasting. To understand the issues with SINGV model, idealized simulations replicating the Weismann & Klemp ‘82 case are conducted keeping similar physics in both the models. Preliminary results indicate that both models behave differently: WRF displays organized convection whereas in SINGV the storm splits at the early stages. Cross-sectional details along the propagating squall line suggest that the updrafts and downdrafts, at the storm development stages, are moderately higher in SINGV compared to WRF. It is speculated that these stronger updrafts in SINGV carry anomalously large amount of liquid water to the upper troposphere where these are converted into rain, which in turn result in stronger downdrafts facilitating the splitting of initial storm. Further analysis is required to conclude our speculation.</p>
Bulletin of the American Meteorological Society, 2016
With a goal of improving operational numerical weather prediction (NWP), the Developmental Testbe... more With a goal of improving operational numerical weather prediction (NWP), the Developmental Testbed Center (DTC) has been working with operational centers, including, among others, the National Centers for Environmental Prediction (NCEP), National Oceanic and Atmospheric Administration (NOAA), National Aeronautics and Space Administration (NASA), and the U.S. Air Force, to support numerical models/systems and their research, perform objective testing and evaluation of NWP methods, and facilitate research-to-operations transitions. This article introduces the first attempt of the DTC in the data assimilation area to help achieve this goal. Since 2009, the DTC, NCEP’s Environmental Modeling Center (EMC), and other developers have made significant progress in transitioning the operational Gridpoint Statistical Interpolation (GSI) data assimilation system into a community-based code management framework. Currently, GSI is provided to the public with user support and is open for contribut...
Journal of the Meteorological Society of Japan. Ser. II, 2018
Background error covariance (BEC) is one of the key components in data assimilation systems for n... more Background error covariance (BEC) is one of the key components in data assimilation systems for numerical weather prediction. Recently, a scheme of using an inhomogeneous and anisotropic BEC estimated from historical forecast error samples has been tested by utilizing the extended alpha control variable approach (BEC-CVA) in the framework of the variational Data Assimilation system for the Weather Research and Forecasting model (WRFDA). In this paper, the BEC-CVA approach is further examined by conducting single observation assimilation experiments and continuous-cycling data assimilation and forecasting experiments covering a 3-week period. Additional benefits of using a blending approach (BEC-BLD), which combines a static, homogeneous BEC and an inhomogeneous and anisotropic BEC, are also assessed. Single observation experiments indicate that the noise in the increments in BEC-CVA can be somehow reduced by using BEC-BLD, while the inhomogeneous and multivariable correlations from BEC-CVA are still taken into account. The impact of BEC-CVA and BEC-BLD on short-term weather forecasts is compared with the threedimensional variational data assimilation scheme (3DVar) and also compared with the hybrid ensemble transform Kalman filter and 3DVar (ETKF-3DVar) in WRFDA. The results show that BEC-CVA and BEC-BLD outperform the use of 3DVar. BEC-CVA and BEC-BLD underperform ETKF-3DVar, as expected. However, the computational cost of BEC-CVA and BEC-BLD is considerably less expensive because no ensemble forecasts are required.
Geoscientific Model Development
This article describes the development and ocean forecast evaluation of an atmosphere-ocean coupl... more This article describes the development and ocean forecast evaluation of an atmosphere-ocean coupled prediction system for the Maritime Continent (MC) domain, which includes the eastern Indian and western Pacific oceans. The coupled system comprises regional configurations of the atmospheric model MetUM and ocean model NEMO at a uniform horizontal resolution of 4.5 km × 4.5 km, coupled using the OASIS3-MCT libraries. The coupled model is run as a pre-operational forecast system from 1 to 31 October 2019. Hindcast simulations performed for the period 1 January 2014 to 30 September 2019, using the stand-alone ocean configuration, provided the initial condition to the coupled ocean model. This paper details the evaluations of ocean-only model hindcast and 6 d coupled ocean forecast simulations. Direct comparison of sea surface temperature (SST) and sea surface height (SSH) with analysis, as well as in situ observations, is performed for the ocean-only hindcast evaluation. For the evaluation of coupled ocean model, comparisons of ocean forecast for different forecast lead times with SST analysis and in situ observations of SSH, temperature, and salinity have been performed. Overall, the model forecast deviation of SST, SSH, and subsurface temperature and salinity fields relative to observation is within acceptable error limits of operational forecast models. Typical runtimes of the daily forecast simulations are found to be suitable for the operational forecast applications.
Quarterly Journal of the Royal Meteorological Society
. This article describes the development and ocean forecast evaluation of an atmosphere-ocean cou... more . This article describes the development and ocean forecast evaluation of an atmosphere-ocean coupled prediction system for the Maritime Continent (MC) domain, which includes the eastern Indian and western Pacific Oceans. The coupled system comprises regional configurations of the atmospheric model MetUM and ocean model NEMO, at a uniform horizontal resolution of 4.5 km × 4.5 km, coupled using the OASIS3-MCT libraries. The coupled model is run as a pre-operational forecast system from 1 to 31 October 2019. Hindcast simulations performed for the period 1 January 2014 to 30 September 2019, using the stand-alone ocean configuration, provided the initial condition to the coupled ocean model. This paper details the evaluations of ocean-only model hindcast and 6-day coupled ocean forecast simulations. Direct comparison of sea surface temperature (SST) and sea surface height (SSH) with analysis as well as in situ observations are performed for the ocean-only hindcast evaluation. For the evaluation of coupled ocean model, comparisons of ocean forecast for different forecast lead times with SST analysis, and in situ observations of SSH, temperature and salinity have been performed. Overall, the model forecast deviation of SST, SSH, and subsurface temperature and salinity fields relative to observation is within acceptable error limits of operational forecast models. Typical runtimes of the daily forecast simulations are found to be suitable for the operational forecast applications.
Weather and Forecasting
Sumatra squalls are important rain-bearing weather systems that affect Singapore and southern Pen... more Sumatra squalls are important rain-bearing weather systems that affect Singapore and southern Peninsular Malaysia. The performance of forecasts for 63 past squall events is evaluated using a subjective evaluation by forecasters and an objective evaluation based on the fractions skill score (FSS). The purpose of this study is to investigate whether an objective procedure can reproduce the main results of the subjective evaluation. A convection permitting version of the Met Office (UKMO) Unified Model (UM), configured for a limited domain in the southern region of the South China Sea, is used with two driving global deterministic models: the UM and the European Centre for Medium-Range Weather Forecasts (ECMWF) model. Subjective and objective evaluation scoring methods for the two limited-area forecasts of the UM are compared, and it is shown that the objective procedure can reasonably emulate the scores produced by the forecasters in the context of parameters that are of direct releva...
Quarterly Journal of the Royal Meteorological Society
SINGV-DA is a convective-scale numerical weather prediction system with regional data assimilatio... more SINGV-DA is a convective-scale numerical weather prediction system with regional data assimilation for Singapore and the surrounding region. This article documents SINGV-DA's current operational configuration and the sensitivity studies that influenced its development. We show that background error covariances derived by bootstrapping (via the lagged National Meteorological Centre method) contain spurious vertical structures at higher model levels that may degrade forecast performance. We found that SINGV-DA precipitation forecasts are sensitive to horizontal resolution and lateral boundary conditions. Our observing system experiments reveal that satellite radiance assimilation, while clearly beneficial for precipitation forecasts in this region, adversely affected model background temperatures and winds at higher altitudes. Benchmarked against the forecast model in isolation, the regional DA system adds significant value to precipitation forecasts in the nowcasting range, but not at longer lead times. Our findings point to the need for further research and development to improve the system. K E Y W O R D S background error covariances, convective-scale, data assimilation, numerical weather prediction, observations, Singapore This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
Journal of Atmospheric and Oceanic Technology
A forecast sensitivity to initial perturbation (FSIP) analysis tool for the WRF Model was develop... more A forecast sensitivity to initial perturbation (FSIP) analysis tool for the WRF Model was developed. The tool includes two modules respectively based on the conditional nonlinear optimal perturbation (CNOP) method and the first singular vector (FSV) method. The FSIP tool can be used to identify regions of sensitivity for targeted observation research and important influential weather systems for a given forecast metric. This paper compares the performance of the FSIP tool to its MM5 counterpart, and demonstrates how CNOP, local CNOP (a kind of conditional nonlinear suboptimal perturbation), and FSV were detected using their evolutions of cost function. The column-integrated features of the perturbations were generally similar between the two models. More significant differences were apparent in the details of their vertical distribution. With Typhoon Matsa (2005) in the western North Pacific and a winter storm in the United States (2000) as validation cases, this work examined the t...
A study of two-dimensional moist convection has been performed with a Boussinesqapproximated mode... more A study of two-dimensional moist convection has been performed with a Boussinesqapproximated model, where the effect of moisture is only taken into account as a flowdependent heating. We first investigate the characteristics of the model in a conditionally unstable stratification. Then by numerically integrating the model equations with a slowly varying static stability, we have also observed hysteretic behaviour as a quasi-equilibrium feature of the model. When the time scale of static stability changes is comparable to reality (I 2 h), the transition from a state of rest to finite amplitude convection is much quicker than the reversed process. This behaviour is directly related to the hysteresis phenomenon and gives a qualitative understanding of the dynamics of moist convection on a diurnal timescale.
Cloud water content (eWC) is not treated in most operational objective analysis and initializatio... more Cloud water content (eWC) is not treated in most operational objective analysis and initialization schemes. \Vhen evve is used as a prognostic variable in a forecast model, it is necessary to define this variable at the initial time. A commonly" used method is to set the initial ewe to zero or use a forecast ewe field from the previous data assimilation cycle (the first-guess field for the objective analysis) without any modification. The inconsitent treatment of ewe and other fields leads to an imbalance between the first-guess cloud water field and other analyzed fields (winds, temperature, humidity and surface pressure). In this study, the diabatic digital filtering initialization scheme is used to alleviate this imbalance. It is shown that an intermittent data assimilation system \vith this initialization scheme can produce a better cloud evolution. a shorter spinup time and a removal of the initial shock in precipitation.
Plateau Meteorology, Apr 24, 2016
Atmosphere
This paper presents a new blending approach to applying large-scale correction to the initial con... more This paper presents a new blending approach to applying large-scale correction to the initial condition in a limited-area numerical weather prediction (NWP) model. The new approach combines the implementation benefits of the known approaches and shows significant improvement in the forecast quality when implemented in a tropical NWP model. Sensitivity studies indicate that many improvements come from blending the horizontal winds alone. Adding temperature and specific humidity to the horizontal winds result in forecast quality degradation in the early hours of the simulated tropical environment.
Quarterly Journal of the Royal Meteorological Society, 2020
The background error covariance matrix plays a vital role in any data assimilation system. Proper... more The background error covariance matrix plays a vital role in any data assimilation system. Proper specification, which is determined by the forecast system setup , is often required. Previous studies have investigated its relevance in various global and regional numerical weather prediction (NWP) systems; however, very few have explored it in tropical NWP systems. Here, we present and evaluate the structures of the background error covariance matrix for a tropical convective-scale NWP system. A total of 12 background error covariance matrices are modelled using differences between pairs of forecasts of different lengths but valid at the same time, based on the application of the vertical-first and horizontal-first transform order formulations on six permutations of the training data. Through pseudo-single observation tests, we extract and test the sensitivity of their structures to the training data period (seasons), forecast lag and transform order. The structures typically exhibit more dependence on forecast lag and transform order; horizontal-first transform order covariances had structures with shorter horizontal length-scales for wind and larger wind background error standard deviations. We also note that some covariances had horizontal and vertical structures with stronger mass-wind coupling, closely resembling an equatorial Kelvin wave. To assess the performance of each of the covariances, 12 month-long data assimilation trials in May 2018 (characterised by frequent occurrences of localised thunderstorm events) are performed. We show improved short-range precipitation forecasts in trials using some of the covariances compared to the current operational covariance. These covariances generally have structures with weak mass-wind coupling, shorter horizontal length-scales for wind and larger wind background error standard deviations, compared to other covariances which led to poorer forecasts. These may be desirable factors when modelling the background error covariance matrix for tropical convective-scale data assimilation systems.
<p>Squall lines are the prominent feature over Singapore region cre... more <p>Squall lines are the prominent feature over Singapore region creating strongly localized rain events due to vigorous localized convective activity. These convective systems have relatively small spatial and temporal scales compared to other atmospheric features like monsoons, thus the prediction of these features lack accuracy. The SINGV numerical weather prediction model is able to provide improved weather forecasts over Singapore region, however, challenges still exist in predicting the thunderstorm/squall line events in onset, location, intensity and lead time. A few real-time case studies of squall lines indicate that SINGV could not capture these features appropriately, while WRF did a better forecasting. To understand the issues with SINGV model, idealized simulations replicating the Weismann & Klemp ‘82 case are conducted keeping similar physics in both the models. Preliminary results indicate that both models behave differently: WRF displays organized convection whereas in SINGV the storm splits at the early stages. Cross-sectional details along the propagating squall line suggest that the updrafts and downdrafts, at the storm development stages, are moderately higher in SINGV compared to WRF. It is speculated that these stronger updrafts in SINGV carry anomalously large amount of liquid water to the upper troposphere where these are converted into rain, which in turn result in stronger downdrafts facilitating the splitting of initial storm. Further analysis is required to conclude our speculation.</p>
Bulletin of the American Meteorological Society, 2016
With a goal of improving operational numerical weather prediction (NWP), the Developmental Testbe... more With a goal of improving operational numerical weather prediction (NWP), the Developmental Testbed Center (DTC) has been working with operational centers, including, among others, the National Centers for Environmental Prediction (NCEP), National Oceanic and Atmospheric Administration (NOAA), National Aeronautics and Space Administration (NASA), and the U.S. Air Force, to support numerical models/systems and their research, perform objective testing and evaluation of NWP methods, and facilitate research-to-operations transitions. This article introduces the first attempt of the DTC in the data assimilation area to help achieve this goal. Since 2009, the DTC, NCEP’s Environmental Modeling Center (EMC), and other developers have made significant progress in transitioning the operational Gridpoint Statistical Interpolation (GSI) data assimilation system into a community-based code management framework. Currently, GSI is provided to the public with user support and is open for contribut...
Journal of the Meteorological Society of Japan. Ser. II, 2018
Background error covariance (BEC) is one of the key components in data assimilation systems for n... more Background error covariance (BEC) is one of the key components in data assimilation systems for numerical weather prediction. Recently, a scheme of using an inhomogeneous and anisotropic BEC estimated from historical forecast error samples has been tested by utilizing the extended alpha control variable approach (BEC-CVA) in the framework of the variational Data Assimilation system for the Weather Research and Forecasting model (WRFDA). In this paper, the BEC-CVA approach is further examined by conducting single observation assimilation experiments and continuous-cycling data assimilation and forecasting experiments covering a 3-week period. Additional benefits of using a blending approach (BEC-BLD), which combines a static, homogeneous BEC and an inhomogeneous and anisotropic BEC, are also assessed. Single observation experiments indicate that the noise in the increments in BEC-CVA can be somehow reduced by using BEC-BLD, while the inhomogeneous and multivariable correlations from BEC-CVA are still taken into account. The impact of BEC-CVA and BEC-BLD on short-term weather forecasts is compared with the threedimensional variational data assimilation scheme (3DVar) and also compared with the hybrid ensemble transform Kalman filter and 3DVar (ETKF-3DVar) in WRFDA. The results show that BEC-CVA and BEC-BLD outperform the use of 3DVar. BEC-CVA and BEC-BLD underperform ETKF-3DVar, as expected. However, the computational cost of BEC-CVA and BEC-BLD is considerably less expensive because no ensemble forecasts are required.
Geoscientific Model Development
This article describes the development and ocean forecast evaluation of an atmosphere-ocean coupl... more This article describes the development and ocean forecast evaluation of an atmosphere-ocean coupled prediction system for the Maritime Continent (MC) domain, which includes the eastern Indian and western Pacific oceans. The coupled system comprises regional configurations of the atmospheric model MetUM and ocean model NEMO at a uniform horizontal resolution of 4.5 km × 4.5 km, coupled using the OASIS3-MCT libraries. The coupled model is run as a pre-operational forecast system from 1 to 31 October 2019. Hindcast simulations performed for the period 1 January 2014 to 30 September 2019, using the stand-alone ocean configuration, provided the initial condition to the coupled ocean model. This paper details the evaluations of ocean-only model hindcast and 6 d coupled ocean forecast simulations. Direct comparison of sea surface temperature (SST) and sea surface height (SSH) with analysis, as well as in situ observations, is performed for the ocean-only hindcast evaluation. For the evaluation of coupled ocean model, comparisons of ocean forecast for different forecast lead times with SST analysis and in situ observations of SSH, temperature, and salinity have been performed. Overall, the model forecast deviation of SST, SSH, and subsurface temperature and salinity fields relative to observation is within acceptable error limits of operational forecast models. Typical runtimes of the daily forecast simulations are found to be suitable for the operational forecast applications.
Quarterly Journal of the Royal Meteorological Society
. This article describes the development and ocean forecast evaluation of an atmosphere-ocean cou... more . This article describes the development and ocean forecast evaluation of an atmosphere-ocean coupled prediction system for the Maritime Continent (MC) domain, which includes the eastern Indian and western Pacific Oceans. The coupled system comprises regional configurations of the atmospheric model MetUM and ocean model NEMO, at a uniform horizontal resolution of 4.5 km × 4.5 km, coupled using the OASIS3-MCT libraries. The coupled model is run as a pre-operational forecast system from 1 to 31 October 2019. Hindcast simulations performed for the period 1 January 2014 to 30 September 2019, using the stand-alone ocean configuration, provided the initial condition to the coupled ocean model. This paper details the evaluations of ocean-only model hindcast and 6-day coupled ocean forecast simulations. Direct comparison of sea surface temperature (SST) and sea surface height (SSH) with analysis as well as in situ observations are performed for the ocean-only hindcast evaluation. For the evaluation of coupled ocean model, comparisons of ocean forecast for different forecast lead times with SST analysis, and in situ observations of SSH, temperature and salinity have been performed. Overall, the model forecast deviation of SST, SSH, and subsurface temperature and salinity fields relative to observation is within acceptable error limits of operational forecast models. Typical runtimes of the daily forecast simulations are found to be suitable for the operational forecast applications.
Weather and Forecasting
Sumatra squalls are important rain-bearing weather systems that affect Singapore and southern Pen... more Sumatra squalls are important rain-bearing weather systems that affect Singapore and southern Peninsular Malaysia. The performance of forecasts for 63 past squall events is evaluated using a subjective evaluation by forecasters and an objective evaluation based on the fractions skill score (FSS). The purpose of this study is to investigate whether an objective procedure can reproduce the main results of the subjective evaluation. A convection permitting version of the Met Office (UKMO) Unified Model (UM), configured for a limited domain in the southern region of the South China Sea, is used with two driving global deterministic models: the UM and the European Centre for Medium-Range Weather Forecasts (ECMWF) model. Subjective and objective evaluation scoring methods for the two limited-area forecasts of the UM are compared, and it is shown that the objective procedure can reasonably emulate the scores produced by the forecasters in the context of parameters that are of direct releva...
Quarterly Journal of the Royal Meteorological Society
SINGV-DA is a convective-scale numerical weather prediction system with regional data assimilatio... more SINGV-DA is a convective-scale numerical weather prediction system with regional data assimilation for Singapore and the surrounding region. This article documents SINGV-DA's current operational configuration and the sensitivity studies that influenced its development. We show that background error covariances derived by bootstrapping (via the lagged National Meteorological Centre method) contain spurious vertical structures at higher model levels that may degrade forecast performance. We found that SINGV-DA precipitation forecasts are sensitive to horizontal resolution and lateral boundary conditions. Our observing system experiments reveal that satellite radiance assimilation, while clearly beneficial for precipitation forecasts in this region, adversely affected model background temperatures and winds at higher altitudes. Benchmarked against the forecast model in isolation, the regional DA system adds significant value to precipitation forecasts in the nowcasting range, but not at longer lead times. Our findings point to the need for further research and development to improve the system. K E Y W O R D S background error covariances, convective-scale, data assimilation, numerical weather prediction, observations, Singapore This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
Journal of Atmospheric and Oceanic Technology
A forecast sensitivity to initial perturbation (FSIP) analysis tool for the WRF Model was develop... more A forecast sensitivity to initial perturbation (FSIP) analysis tool for the WRF Model was developed. The tool includes two modules respectively based on the conditional nonlinear optimal perturbation (CNOP) method and the first singular vector (FSV) method. The FSIP tool can be used to identify regions of sensitivity for targeted observation research and important influential weather systems for a given forecast metric. This paper compares the performance of the FSIP tool to its MM5 counterpart, and demonstrates how CNOP, local CNOP (a kind of conditional nonlinear suboptimal perturbation), and FSV were detected using their evolutions of cost function. The column-integrated features of the perturbations were generally similar between the two models. More significant differences were apparent in the details of their vertical distribution. With Typhoon Matsa (2005) in the western North Pacific and a winter storm in the United States (2000) as validation cases, this work examined the t...
A study of two-dimensional moist convection has been performed with a Boussinesqapproximated mode... more A study of two-dimensional moist convection has been performed with a Boussinesqapproximated model, where the effect of moisture is only taken into account as a flowdependent heating. We first investigate the characteristics of the model in a conditionally unstable stratification. Then by numerically integrating the model equations with a slowly varying static stability, we have also observed hysteretic behaviour as a quasi-equilibrium feature of the model. When the time scale of static stability changes is comparable to reality (I 2 h), the transition from a state of rest to finite amplitude convection is much quicker than the reversed process. This behaviour is directly related to the hysteresis phenomenon and gives a qualitative understanding of the dynamics of moist convection on a diurnal timescale.
Cloud water content (eWC) is not treated in most operational objective analysis and initializatio... more Cloud water content (eWC) is not treated in most operational objective analysis and initialization schemes. \Vhen evve is used as a prognostic variable in a forecast model, it is necessary to define this variable at the initial time. A commonly" used method is to set the initial ewe to zero or use a forecast ewe field from the previous data assimilation cycle (the first-guess field for the objective analysis) without any modification. The inconsitent treatment of ewe and other fields leads to an imbalance between the first-guess cloud water field and other analyzed fields (winds, temperature, humidity and surface pressure). In this study, the diabatic digital filtering initialization scheme is used to alleviate this imbalance. It is shown that an intermittent data assimilation system \vith this initialization scheme can produce a better cloud evolution. a shorter spinup time and a removal of the initial shock in precipitation.