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On the development of the GRAPES—A new generation of the national operational NWP system in China


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Numerical weather prediction (NWP) has become one of the most important means for weather forecasts in the world. It also mirrors a nation’s comprehensive strength in meteorology. In 2000, China Meteorological Administration (CMA) established the National Innovative Base for Meteorological Numerical Prediction in the Chinese Academy of Meteorological Sciences (CAMS), to work on developing a new generation of the national operational NWP system—Global/Regional Assimilation and PrEdiction System (GRAPES), to enhance meteorological services in China in the new century. In recent years, the GRAPES has witnessed a fast development. The GRAPES has been set up as an integration of the model framework, data assimilation, regional and global NWP system, which can be commonly used for both operation and research. In this paper, a brief review is made for illustrating the GRAPES system, including the advanced designs of the GRAPES, its diverse applications in multi-fields, and efficiencies of the regional and global GRAPES in operational applications based on hindcast results.
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Chinese Science Bulletin
Springer | | Chinese Science Bulletin | November 2008 | vol. 53 | no. 22 | 3429-3432
On the development of the GRAPES――
A new generation of the national operational
NWP system in China
ZHANG RenHe & SHEN XueShun
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Numerical weather prediction (NWP) has become one of the most important means for weather fore-
casts in the world. It also mirrors a nation’s comprehensive strength in meteorology. In 2000, China
Meteorological Administration (CMA) established the National Innovative Base for Meteorological
Numerical Prediction in the Chinese Academy of Meteorological Sciences (CAMS), to work on devel-
oping a new generation of the national operational NWP system Global/Regional Assimilation and
PrEdiction System (GRAPES), to enhance meteorological services in China in the new century. In re-
cent years, the GRAPES has witnessed a fast development. The GRAPES has been set up as an inte-
gration of the model framework, data assimilation, regional and global NWP system, which can be
commonly used for both operation and research. In this paper, a brief review is made for illustrating
the GRAPES system, including the advanced designs of the GRAPES, its diverse applications in
multi-fields, and efficiencies of the regional and global GRAPES in operational applications based on
hindcast results.
GRAPES, numerical weather prediction, China national operational weather forecasts
China is a country frequently attacked by meteorological
disasters. Meteorological disasters have brought up huge
losses to the nation’s economy, social development, and
people’s lives and properties. Since the 1990s, each year
China would have some 48 million hectares of croplands
attacked by meteorological disasters, with a disaster af-
fected population of about 380 million, and a direct
economic loss worth about RMB 180 billion, or 2.7% as a
proportion of GDP[1]. Raising the accuracy of predicting
severe weathers makes a most direct and effective ap-
proach to mitigate and prevent meteorological disasters.
Since the 1950s, the numerical weather prediction (NWP),
building on the bases of mathematics and physics, has
been one of major accomplishments achieved in the area
of atmospheric sciences[2]. Thanks to more than half a
century development, the NWP has found wide applica-
tions in the world, and has become one of the most im-
portant means in operational weather forecasts.
The development of the NWP depends not only on
the basic researches of the atmospheric sciences, but
also on the space based sounding and retrieval tech-
niques for collecting global atmospheric data, super-
computer technologies that make data processing and
computation more efficient, and telecommunication
technologies that allow a fast dissemination of data. In
this context, a nation’s NWP level mirrors the compre-
hensive strength of these technologies, in addition to the
level of researches on the atmospheric sciences. It also
marks the level of a nation’s meteorological moderniza-
Received June 18, 2008; accepted August 27, 2008
doi: 10.1007/s11434-008-0462-7
Corresponding author (email:
Supported by Key Technologies Research & Development Program (Grant No.
2001BA607B), National Key Technology Research and Development Program
(Grant No. 2006BAC02B02) and National Basic Research Program of China (Grant
No. 2004CB418300)
3430 ZHANG RenHe et al. Chinese Science Bulletin | November 2008 | vol. 53 | no. 22 | 3429-3432
tion, and comprehensive strength in the area of meteor-
China started the national operation of the NWP at
around the end of 1980s and the beginning of 1990s
with the main framework of the NWP models being in-
troduced from the developed countries. The introduced
operational models did not offer needed continuity and
re-development functionality. As a result, China’s NWP
operation deplored a large gap compared with the de-
veloped countries, in a range of indicators including
model’s performance, data assimilation, and accuracy of
prediction, which in turn hindered the improvement of
China’s operational meteorological services, and became
a bottleneck restricting the development of China’s me-
teorological modernization. To realize a leaping devel-
opment in the area, meet the increasing needs of eco-
nomic and social development for meteorological ser-
vices, and to work on a fully modernized operational
NWP system, China Meteorological Administration es-
tablished in October 2000 the National Innovative Base
for Meteorological Numerical Prediction at the Chinese
Academy of Meteorological Sciences, in a move to cre-
ate a proprietary Global/Regional Assimilation and PrE-
diction System (GRAPES) in the new century. Both
Ministry of Science and Technology of China and China
Meteorological Administration nodded their approval to
create projects for the purpose, with further support for
the related basic researches from National Natural Sci-
ence Foundation of China. Recent years have witnessed
a fast development of the GRAPES mainly on the efforts
of Chinese scientists[3 5]. The achievements include
model framework, data assimilation, and integrated re-
gional/global numerical forecast system both for re-
search and operation. This issue of the Chinese Science
Bulletin releases 5 GRAPES related papers[610], to re-
flect the latest development in the area. The following
presents a brief review of the development of the
GRAPES, illustrating its advanced natures, its diverse
applications in multi-fields, and efficiencies of the re-
gional and global GRAPES in operational applications
based on hindcast results.
1 Advanced natures of the GRAPES
The GRAPES is built using latest achievements and the
experience derived from the development of operational
NWP systems in the world after the late 1990s. The ad-
vanced features of GRAPES are mainly in the following
The principle of the intensiveness is applied in the
development of the GRAPES. The GRAPES framework
incorporates the dynamic core of multi-scales, which
can be used as a common base for different application
models. The dynamic core is designed with a range of
options including static or non-static, global or regional,
and optional horizontal or vertical resolutions. An inte-
grated model system capable of diverse forecast applica-
tions is developed. Compared with individual target
based models, it is more desirable for long-term techni-
cal upgrade and development. Modular and standardized
coding is adopted so that it is easy for enlarging the
model functions, allowing scalable and sustainable de-
velopment, reducing the cost of the continuous devel-
opment, and easily transferring research findings into
operational applications. The variational assimilation
system of the GRAPES can assimilate not only the
regular meteorological observational data, but also satel-
lite based direct radiation data, remote sensing data de-
rived from the Doppler weather radar and products from
the remote sensing of the satellite (cloud winds, for ex-
ample). The variational assimilation system in the
GRAPES greatly increases the data that can be used in
the NWP in China, creating a solid ground for raising
the accuracy of the model forecast.
2 Diverse applications
In China, based on the GRAPES, an array of numerical
forecast systems has been developed for different appli-
cations. The GRAPES mesoscale NWP system
(GRAPES-Meso) has been put into the national opera-
tion in 2006 at the National Meteorological Center. The
GRAPES typhoon forecast model (GRAPES-TCM) has
fine performance for predicting route related major ele-
ments of the tropical cyclone (TC), including route type,
intensity, landing process, and sudden changes of the
moving direction and the wind speed of TC[11]. The
GRAPES sandstorm forecast model (GRAPES-SDM)
performs well. It can predict basically the occurrence
and development of sand and dust storms in China[12].
The GRAPES lightening forecast model, nesting a
lightening module in the GRAPES-Meso, has success-
fully simulated several lightening cases occurring in
Beijing. The simulation results show that strong convec-
tive centers and high concentration areas of ice particles
correspond to strong charge centers, in agreement with
ZHANG RenHe et al. Chinese Science Bulletin | November 2008 | vol. 53 | no. 22 | 3429-3432 3431
actual observations, demonstrating an enhanced poten-
tial for predicting lightening[13]. The explicit cloud-pre-
cipitation scheme nested in the GRAPES can predict the
distribution of the cloud as well as the cloud structure
and phases in the cloud, which has been used in provid-
ing guidance for weather modification activities in
China[14]. The GRAPES Severe Weather Integrated Tool
(GRAPES-SWIFT) has joined the project of the World
Meteorological Organization WMO/B08FDP/RDP as
the only short-term and nowcasting weather prediction
system developed by Chinese scientists among other
model systems from different countries, providing me-
teorological services for the Beijing Olympic Game in
3 Hindcast experiments of the GRAPES
NWP system
The GRAPES-Meso system with a 30 km horizontal
resolution was put into operation in 2006 at the National
Meteorological Center. The system was upgraded to a
15 km horizontal resolution in 2007. In order to demon-
strate the forecast effectiveness, Figure 1 shows the ETS
scores for 24-h continuous hindcasts of the precipitation
in the region of whole China for a total year from June
2006 to May 2007. In Figure 1, for comparison a num-
ber of operational models from domestic and overseas
are used to make the precipitation hindcasts. Six models
are employed including GRAPES-Meso (30 km/15 km
horizontal resolution), MM5 regional model (27 km
horizontal resolution) and T213 global model (about 60
km horizontal resolution) used for operational forecasts
in China, Japan’s model (about 20 km horizontal resolu-
tion), and German model (40 km horizontal resolution).
From Figure 1 we can see that, compared with the
version of the 30 km horizontal resolution, it is apparent
the GRAPES-meso model of the 15 km version yields
the better result for the hindcasted light rainfall (<0.1
mm), moderate rainfall (0.110 mm), and heavy rain-
fall (1025 mm), respectively. Compared with other
models, GRAPES-Meso with the 15 km horizontal reso-
lution exhibits a fine systematic strength. The ETS
scores are generally higher than those of other models
except the score for the light rainfall being only a bit
lower than that of its German counterpart, moderate
rainfall a bit lower than the Japanese model, and heavy
rainfall lower than MM5. Comparison results also show
that GRAPES-Meso with the 15 km horizontal resolu-
tion is able to produce 24-h precipitation forecasts in the
region of China equivalent to or even better than those
offered by its advanced international counterparts.
In order to show the forecast effectiveness of the
GRAPES Global Forecast System (GRAPES-GFS), the
hindcasts given by the GRAPES-GFS with the 100 km
horizontal resolution are made continuously for a whole
year from December 2006 to November 2007. Figure 2
gives the anomaly correlation coefficients (ACC) be-
tween the hindcast results of the GRAPES-GFS and the
analyses of the National Center for Environmental Pre-
diction of USA (NCEP). The ACC is calculated based
on the 500 hPa geopotential height fields in the northern
hemisphere in the latitudinal zone of 20°90°N in the
period of December 2006November 2007.
From Figure 2 we can see that the GRAPES-GFS has
obviously possessed a fine capability for the prediction.
Figure 1 ETS scores for 24 h hindcasts of precipitations of the light (<0.1 mm, left panel), moderate (0.110 mm, middle panel), and heavy (10
25 mm, right panel) rainfall in China region during the period of June 2006May 2007 by using different domestic and overseas models.
3432 ZHANG RenHe et al. Chinese Science Bulletin | November 2008 | vol. 53 | no. 22 | 3429-3432
Figure 2 ACC between GRAPES-GFS hindcasts and NCEP analyses
for 500 hPa geopotential height fields in the northern hemisphere (20°
90°N) during the period of December 2006November 2007.
The ACC between the GRAPES-GFS hindcasts and
NCEP analyses has reached 0.97, 0.94 and 0.87 for the
GRAPES-GFS hindcast of 24 h, 48 h and 60 h, respec-
tively. When taking 0.6 ACC as a threshold value of the
effectiveness for forecasts, the GRAPES-GFS has sus-
tained the capability of the effective forecast time about
some 6 days, which is basically qualified for an opera-
tional application. As a result, GRAPES-GFS will be put
into a trial operation at the National Meteorological
Center in the near future.
4 Concluding remarks
The GRAPES is a homemade numerical meteorological
prediction system. The satellite data assimilation tech-
nique employed in the system has found a solution to
addressing technical bottlenecks restricting the applica-
tion of the satellite data in numerical weather prediction
in China. The common used multi-scale dynamical core
and static or non-static equilibrium framework have cre-
ated a ground for intensively developing the numerical
model. In addition, the non-static meso-scale forecast
model with sophisticated physical processes has paved
the way for predicting meso-scale severe weathers in
China. The successful establishment of the global pre-
diction system has made China one of the countries that
have mastered the core technologies in building the
global NWP model.
The GRAPES still needs further optimization during
operational applications, and original innovations tai-
lored to special weather in China[15], though it has so far
achieved noticeable progresses. Efforts shall also be
made to optimize the algorithms handling large terrains
in the dynamic framework, especially the topography
around the Tibetan Plateau, which affects weather sig-
nificantly in China. The GRAPES should expand to the
stratosphere and coupling with ocean models, and de-
velop the land process model specially for China’s so-
phisticated surface conditions, and the two-way nested
global and regional model. As far as physical processes
are concerned, the tailored physical parameterized
schemes shall be worked out in the GRAPES to reflect
the characteristics in East Asia where China is located at.
In the data assimilation system, the assimilation of di-
verse observations should be realized, and special atten-
tion shall be paid to utilizing the domestic observational
data. With operation oriented efforts, the GRAPES will
eventually be built into an advanced international NWP
system with Chinese characteristics.
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The existence of outliers can seriously influence the analysis of variational data assimilation. Quality control allows us to effectively eliminate or absorb these outliers to produce better analysis fields. In particular, variational quality control (VarQC) can process gray zone outliers and is thus broadly used in variational data assimilation systems. In this study, governing equations are derived for two VarQC algorithms that utilize different contaminated Gaussian distributions (CGDs): Gaussian plus flat distribution and Huber norm distribution. As such, these VarQC algorithms can handle outliers that have non-Gaussian innovations. Then, these VarQC algorithms are implemented in the Global/Regional Assimilation and PrEdiction System (GRAPES) model-level three-dimensional variational data assimilation (m3DVAR) system. Tests using artificial observations indicate that the VarQC method using the Huber distribution has stronger robustness for including outliers to improve posterior analysis than the VarQC method using the Gaussian plus flat distribution. Furthermore, real observation experiments show that the distribution of observation analysis weights conform well with theory, indicating that the application of VarQC is effective in the GRAPES m3DVAR system. Subsequent case study and long-period data assimilation experiments show that the spatial distribution and amplitude of the observation analysis weights are related to the analysis increments of the mass field (geopotential height and temperature). Compared to the control experiment, VarQC experiments have noticeably better posterior mass fields. Finally, the VarQC method using the Huber distribution is superior to the VarQC method using the Gaussian plus flat distribution, especially at the middle and lower levels.
... The mesoscale version of the new-generation Global/Regional Assimilation and Prediction System (GRAPES_Meso) is an operational weather forecasting model developed by the Chinese Academy of Meteorological Sciences, which mainly consists of 3-D mete-orological field data assimilation, the full compressible dynamic framework, and a physical parameterization package [40][41][42]. This model is generally used for operational forecasting and scientific research about the medium-and short-term weather conditions in China [43,44]. It has also been widely used for the study of cloud, aerosol, and the interaction between cloud and aerosol in China for about 20 years [45,46]. ...
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In the present study, the Morrison double-moment cloud microphysics scheme including mass and droplet number concentration of water and ice clouds is implemented into the Chinese mesoscale version of the Global/Regional Assimilation and Prediction System (GRAPES_Meso). Sensitivity experiments of different cloud condensation nuclei (CCN) values are conducted to study the impacts of CCN on cloud microphysical processes and radiation processes in East China. The model evaluation shows that the simulated cloud liquid water path (CLWP) is consistent with that of the National Center for Environment Prediction (NCEP) reanalysis, and the cloud optical depth (COD) and effective radius of cloud water (Rc) are in agreement with those of the Moderate Resolution Imaging Spectroradiometer (MODIS) datasets both in regional distribution and magnitudes. These comparisons illustrate the effectiveness of the Morrison scheme for the cloud processes in East China. For the study period of 8 to 12 October 2017, the sensitivity experiments show that with initial CCN number concentration (CCN0) increasing from 10 to 3000 cm−3, the maximum value of daily average Rc decreases by about 63%, which leads to a decrease of cloud-rain conversion rate. Moreover, the maximum value of daily average mixing ratio of cloud water (qc) increases by 133%, the maximum value of daily average mixing ratio of rain (qr) decreases by 44%, and the maximum value of daily average CLWP and COD increase by 100% and 150%, respectively. This results in about 65% increasing of the maximum value of daily average cloud downward shortwave radiative forcing (CDSRF) when CCN0 increases from 10 cm−3 to 3000 cm−3. The study indicates the important impacts of CCN on cloud properties and radiation effects.
A nonlinear artificial intelligence ensemble forecast model has been developed in this paper for predicting tropical cyclone (TC) tracks based on the deep neural network (DNN) by using the 24-h forecast data from the China Meteorological Administration (CMA), Japan Meteorological Agency (JMA) and Joint Typhoon Warning Center (JTWC). Data from a total of 287 TC cases over the Northwest Pacific Ocean from 2004 to 2015 were used to train and validate the DNN based ensemble forecast (DNNEF) model. The comparison of model results with Best Track data of TCs shows that the DNNEF model has a higher accuracy than any individual forecast center or the traditional ensemble forecast model. The average 24-h forecast error of 82 TCs from 2016 to 2018 is 63 km, which has been reduced by 17.1%, 16.0%, 20.3%, and 4.6%, respectively, compared with that of CMA, JMA, JTWC, and the error-estimation based ensemble method. The results indicate that the nonlinear DNNEF model has the capability of adjusting the model parameter dynamically and automatically, thus improving the accuracy and stability of TC prediction.
We presented the development of the gaseous chemistry adjoint module of the meteorological-chemical model system GRAPES-CUACE (Global/Regional Assimilation and PrEdiction System coupled with CMA Unified Atmospheric Chemistry Environmental Forecasting System) on the basis of the previously constructed aerosol adjoint module. The latest version of the GRAPES-CUACE adjoint model mainly includes the adjoint of the physical and chemical processes, the adjoint of the transport processes, and the adjoint of interface programs, of both gas and aerosol. The adjoint implementation was validated for the full model, and adjoint results showed good agreement with brute force sensitivities. We also applied the newly developed adjoint model to the sensitivity analysis of an ozone episode occurred in Beijing on July 2, 2017, as well as the design of emission-reduction strategies for this episode. The relationships between the ozone concentration and precursor emissions were well captured by the adjoint model. It is indicated that for a case used here, the Beijing peak ozone concentration was influenced mostly by local emissions (6.2-24.3%), as well as by surrounding emissions, including Hebei (4.4-16.8%), Tianjin (1.8-6.6%), Shandong (1.8-2.6%), and Shanxi (<1%). In addition, reduction of NOx, VOCs, and CO emissions in these regions would effectively decrease the Beijing peak ozone concentration. This study highlights the capability of GRAPES-CUACE adjoint model in quantifying "emission-concentration" relationship and in providing guidance for environmental control policy.
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During the past few years, most of the new developed numerical weather prediction models adopt the strategy of multi-scale technique. Therefore, China Meteorological Administration has devoted to developing a new generation of global and regional multi-scale model since 2003. In order to validate the performance of the GRAPES (Global and Regional Assimilation and PrEdiction System) model both for its scientific design and program coding, a suite of idealized tests has been proposed and conducted, which includes the density flow test, three-dimensional mountain wave and the cross-polar flow test. The density flow experiment indicates that the dynamic core has the ability to simulate the fine scale nonlinear flow structures and its transient features. While the three-dimensional mountain wave test shows that the model can reproduce the horizontal and vertical propagation of internal gravity waves quite well. Cross-polar flow test demonstrates the rationality of both for the semi-Lagrangian departure point calculation and the discretization of the model near the poles. The real case forecasts reveal that the model has the ability to predict the large-scale weather regimes in summer such as the subtropical high, and to capture the major synoptic patterns in the mid and high latitudes.
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This paper summarizes the recent progress of numerical weather prediction (NWP) research since the last review was published. The new generation NWP system named GRAPES (the Global and Regional Assimilation and Prediction System), which consists of variational or sequential data assimilation and nonhydrostatic prediction model with options of configuration for either global or regional domains, is briefly introduced, with stress on their scientific design and preliminary results during pre-operational implementation. In addition to the development of GRAPES, the achievements in new methodologies of data assimilation, new improvements of model physics such as parameterization of clouds and planetary boundary layer, mesoscale ensemble prediction system and numerical prediction of air quality are presented. The scientific issues which should be emphasized for the future are discussed finally.
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According to the modularization and standardization of program structure in Global/Regional Assimilation and Prediction System (GRAPES), the plug-compatible and transplantable regional meso-scale and global middle-range physics software package is established. The package’s component integrality is comparative with the other advanced models physics. A three-level structure of connecting GRAPES physics and dynamic frame has been constructed. The friendly interface is designed for users to plug in their own physics packages. Phenomenon of grid-point storm rainfall in numerical prediction is analyzed with the numerical tests. The scheme of air vertical velocity calculation is improved. Optimizing tests of physics schemes are performed with the correlative parameters adjusting. The results show that the false grid-point storm rainfall is removed by precipitation scheme improving. Then the score of precipitation forecast is enhanced.
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We examined the expression of the dopamine transporter in rat and bullfrog retinas by immunohistochemistry. In both species, the dopamine transporter was strongly expressed in somata and processes of all dopaminergic amacrine cells. In contrast, no immunoreactivity for dopamine transporter was observed in cholinergic amacrine cells. In rat dopaminergic interplexiform cells, dopamine transporter immunoreactivity was also observed on the ascending processes terminating in the outer plexiform layer. Furthermore, the labeling for dopamine transporter diffusely appeared in both outer and inner plexiform layers. This expression profile of the dopamine transporter suggests that dopamine may be taken up not only in the synapses but also extrasynaptically by dopamine transporter, diffusely distributed in both plexiform layers.
Variational method is capable of dealing with observations that have a complicated nonlinear relation with model variables representative of the atmospheric state, and so make it possible to directly assimilate such measured variables as satellite radiance, which have a nonlinear relation with the model variables. Assimilation of any type of observations requires a corresponding observation operator, which establishes a specific mapping from the space of the model state to the space of observation. This paper presents in detail how the direct assimilation of real satellite radiance data is implemented in the GRAPES-3DVar analysis system. It focuses on all the components of the observation operator for direct assimilation of real satellite radiance data, including a spatial interpolation operator that transforms variables from model grid points to observation locations, a physical transformation from model variables to observed elements with different choices of model variables, and a data quality control. Assimilation experiments, using satellite radiances such as NOAA17 AMSU-A and AMSU-B (Advanced Microwave Sounding Unit), are carried out with two different schemes. The results from these experiments can be physically understood and clearly reflect a rational effect of direct assimilation of satellite radiance data in GRAPES-3DVar analysis system.
A new generation of numerical prediction system GRAPES (a short form of Global/Regional Assimilation and PrEdiction System) was set up in China Meteorological Administration (CMA). This paper focuses on the scientific design and preliminary results of the numerical prediction model in GRAPES, including basic idea and strategy of the general scientific design, multi-scale dynamic core, physical package configuration, architecture and parallelization of the codes. A series of numerical experiments using the real data with horizontal resolutions from 10 to 280 km and idealized experiments with very high resolution up to 100 m are conducted, giving encouraging results supporting the multi-scale application of GRAPES. The results of operational implementation of GRAPES model in some NWP centers are also presented with stress at evaluations of the capability to predict the main features of precipitation in China. Finally the issues to be dealt with for further development are discussed.
The scientific design and preliminary results of the data assimilation component of the Global-Regional Prediction and Assimilation System (GRAPES) recently developed in China Meteorological Administration (CMA) are presented in this paper. This is a three-dimensional variational (3DVar) assimilation system set up on global and regional grid meshes favorable for direct assimilation of the space-based remote sensing data and matching the frame work of the prediction model GRAPES. The state variables are assumed to decompose balanced and unbalanced components. By introducing a simple transformation from the state variables to the control variables with a recursive or spectral filter, the convergence rate of iteration for minimization of the cost function in 3DVar is greatly accelerated. The definition of dynamical balance depends on the characteristic scale of the circulation considered. The ratio of the balanced to the unbalanced parts is controlled by the prescribed statistics of background errors. Idealized trials produce the same results as the analytic solution. The results of real data case studies show the capability of the system to improve analysis compared to the traditional schemes. Finally, further development of the system is discussed.
An overview on recent progresses of the operational numerical weather prediction models (in Chinese)
  • D H Chen
  • J Xue
  • D. H. Chen
Chen D H, Xue J S. An overview on recent progresses of the operational numerical weather prediction models (in Chinese). Acta Meteor Sinica, 2004, 62(5): 623-633
Recent progress on GRAPES research and application (in Chinese)
  • D H Chen
  • X Shen
  • D. H. Chen
Chen D H, Shen X S. Recent progress on GRAPES research and application (in Chinese). J Appl Meteor Sci, 2006, 17(6): 773-777