CN110703357A

CN110703357A - Global medium term numerical forecast (GRAPES _ GFS)

Info

Publication number: CN110703357A
Application number: CN201910357780.8A
Authority: CN
Inventors: 沈学顺; 薛纪善; 陈德辉; 韩威; 胡江林; 刘永柱; 孙健; 张�林; 金之雁; 苏勇; 王金成; 龚建东; 张华�; 张红亮; 刘奇俊; 陈起英; 田伟红; 胡江凯; 周斌; 赵滨
Original assignee: Guo Jiaqixiangzhongxin
Current assignee: Guo Jiaqixiangzhongxin
Priority date: 2019-04-30
Filing date: 2019-04-30
Publication date: 2020-01-17
Anticipated expiration: 2039-04-30
Also published as: CN110703357B

Abstract

The invention discloses a global medium-term numerical prediction (GRAPES _ GFS), which is characterized in that the existing framework is improved in the aspect of large-scale parallel computation aiming at the defects of the existing semi-implicit semi-Lagrange integration scheme in the aspects of computation efficiency and expandability, and a solving algorithm with high efficiency and high performance is provided based on a semi-implicit integration format and a linearization system. The non-equidistant difference is adopted, so that the calculation precision of the background temperature profile is obviously improved, and the problem of large background temperature profile error on the level of severe change of the mode vertical layering thickness is solved; the digital filtering module is reconstructed and optimized, so that the stability and the calculation efficiency of digital filtering are improved; A3D-Var integrated single-point test system is established.

Description

Global medium term numerical forecast (GRAPES _ GFS)

Technical Field

The invention belongs to the technical field of Global medium-term numerical weather forecast, and particularly relates to a Global medium-term numerical forecasting method (Global/Global assessment and Prediction System) of GRAPES (Global forecasting System), which is called GRAPES _ GFS for short.

Background

At present, a global medium-term numerical weather forecast system is the core of a numerical forecast service system, provides boundary conditions and background information for scale numerical forecast in a region, and is also the basis of global collective forecast. The continuous development of the global middle-term forecasting mode and the assimilation technology pushes the overall research and the continuous improvement of the forecasting level of the world numerical forecasting to a great extent. Therefore, the development and continuous improvement of the global medium term forecasting system are key tasks of the weather forecasting service centers in the world. The constrained satellite data deviation correction technology has obvious contribution to better utilizing observation information, improving the utilization rate of satellite data and improving the analysis precision. Traditional observation bias correction relies mainly on the difference between observation and background, and there are two main problems in business applications: (1) when the mode has significant deviation, the observation information can be corrected by mistake; (2) when quality control is not perfect, unreasonable estimation and correction of observation bias may result. However, the quasi-service version GRAPES _ GFS 1.0 shows the phenomena of unstable shape potential field forecasting skills, too fast attenuation of forecasting skills in the first three days, low accuracy of an initial value obtained by assimilation, large forecasting deviation and low score of forecasting weak rainfall TS (Threat score) in the continental area of China in the later service operation process, and is similar to the T (threshold score) put into service in 2009_L639 the mode is far from the standard, especially in east asian areas, with low prediction skills. In particular, the pattern prediction is unstable in many cases. The reason is related to that some basic algorithms and technical details in the GRAPES _ GFS global assimilation forecast system are not reasonably and correctly solved. In terms of a global mode, the semi-implicit semi-Lagrange algorithm of a power framework has low calculation precision and does not consider how to improve quality conservation, the calculation noise processing related to the semi-Lagrange scheme is not fully considered in algorithm design, large terrain processing and a sub-networkGrid terrain dynamic action is not considered enough, mode physical process optimization is not enough, mutual coordination and balance among physical processes related to cloud and precipitation are not evaluated and adjusted carefully, and forecasting stability and precision of the mode are seriously influenced. In terms of a global assimilation system, the problem that unnecessary interpolation errors, inconsistency of analysis variables and forecast variables, lack of reasonable estimation and optimization of background error covariance, rough quality control of observed data and a deviation correction technology, and lack of a high-quality assimilation application technology of satellite data exist in an assimilation mode space due to the analysis of an isobaric surface adopted by a quasi-business version influences the assimilation precision.

Disclosure of Invention

The present invention aims to provide a global medium term numeric forecast (GRAPES _ GFS) that overcomes the above-mentioned technical problems, said method of the invention comprising the following steps:

step 1, establishing a GRAPES _ GFS global medium term numerical forecasting system, which comprises a data assimilation module, a dynamic framework module, a physical process module, a parallel computation module, a data preprocessing module and an operation script module.

Step 2, adopting non-interpolation potential temperature Lagrange advection calculation, optimization of polar region filtering scheme, introduction of a scalar advection scheme with terrain filtering considering effective terrain and high-precision conservation, development of quality conservation correction, terrain filtering, w-damming and introduction of stratospheric Rayleigh friction effect to improve the stability, quality conservation and calculation precision of the mode power frame; and (3) adopting an independently developed conformal positive constant conservation semi-Lagrange scalar advection scheme and a polar region processing technology to improve the calculation precision of the mode wet physical quantity field.

Step 3, realizing conservation according to a semi-Lagrange mode and an Euler flux form equation set so as to provide a scalar advection scheme PRM (Piecewise Rational method) based on a segmented Rational function;

3.1, solving a water vapor equation according to a flux form, and updating the flux of the grid interface by adopting a semi-Lagrange algorithm;

and 3.2, calculating in the polar region aiming at the flux form forecasting equation set so as to deduce a mixed scheme of the polar region PRM and a Quasi-Monotone alignment Lagrangian advection scheme QMSL (Quasi-Monoto Semi-Lagrangian) to ensure the precision and the rationality of the calculation of the wet physical quantity of the polar region.

Step 4, upgrading twice and deeply optimizing once in the aspect of the physical process to form a physical process package;

step 4.1, upgrading a physical process package which comprises RRTMG (Rapid Radiative Transfer Model for GCMs) radiation and introduction of a CoLM (common Land Model) Land process for global atmospheric circulation simulation forecast for the first time;

step 4.2, the second upgrade improves the cloud convection and boundary layer process from the physical mechanism, develops a macro-closed scheme based on a double-parameter cloud physical scheme, introduces sub-grid-scale terrain gravity wave parameterization to improve the mode for forecasting tropical precipitation and global cloud amount, and can slow down the south-east wind forecast deviation in China and improve the mode for forecasting the situation field;

4.3, improving the calling calculation of the boundary layer process on the basis of the step 4.1 and the step 4.2, and avoiding errors caused by interpolation of the physical process tendency from a Lorenz jumping point to a Charney-Phillips jumping point;

step 4.4, improving the temperature forecast deviation over the land according to the direct radiation effect of the aerosol in the radiation calculation, and adopting an ice distribution weather field in a Hadley center;

step 4.5, optimizing sea ice distribution in the CoLM and improving albedo of the sea ice;

step 4.6, improving the calculation of the land surface albedo according to an ECMWF (European Centre for Medium-Range Weather forms) mode;

and 4.7, correcting the problems of mode integral instability possibly caused in the initialization of the lake mode and the calculation of the leaf surface temperature in the optimization process so as to strengthen the calculation stability of the mode.

And 5, increasing roll-out feedback of large-scale macro cloud, deep convection and shallow convection and explicit cloud amount forecast based on a double-parameter cloud micro physical process to form a complete global mode cloud computing scheme.

Step 6, forecasting equations of the water content, the particle number concentration and the cloud amount of the hydraulics in the cloud scheme of the GRAPES _ GFS global assimilation forecasting system are as follows:

q in the above equations (1), (2) and (3)_xWater contents representing water vapor and five water condensates of cloud water, rain water, cloud ice, snow and aragonite, N_xRepresenting four other particle number concentrations than moisture and cloud droplets, and a represents the cloud size. At present, only cloud water q is considered for the influence of the large-scale cloud condensation process and the deep and shallow convection rolling-out_cAnd Yunying ice q_iIgnoring the situation for snow q_sRain drops q_rAnd aragonite q_gAnd the effect of various particle number concentrations;

the influence items of the flow rolling-out process on the lattice point scale cloud are as follows:

wherein D_upRepresenting the lift-off rate of updraft in the cloud, M_upRepresenting the mass flux of the updraft,/_xRepresenting the content of cloud water or cloud ice in the ascending air flow in the cloud, q_xThe water content of the cloud water or cloud ice is the lattice point scale; wherein Cloud micro-physics and macro cloud source-sink terms, respectively.

Step 7, adopting an increment analysis scheme by a GRAPES _ GFS mode space 3D-Var (Three-Dimension Variation) analysis system:

wherein δ x ═ x_a-x_b，x_aAnd x_bIs a column vector composed of mode variables, which respectively represent an analysis field and a background field; b is the error covariance matrix of the background field; h is an observation operator, which converts the mode variables into observation equivalent quantities; r is the observed error covariance matrix, d is the observed increment; the coordinate and the variable definition of the 3D-Var of the pattern space are completely matched with the GRAPES _ GFS forecast pattern, and the analysis variable is the same as the state variable of the pattern; and converting the analysis variable into a control variable irrelevant to the variables by adopting a variable transformation method so as to reduce the dimension of the covariance of the background error.

And step 8, adopting the information of satellite radiance data calibration as prior constraint to deduce a deviation correction method with constraint on satellite radiance data.

And 9, designing a read-write mode of the parallel IO to solve the problem that the overall speed is slower when the parallel scale of the existing serial IO is larger, and greatly improving the efficiency of mode data input and output.

The global middle-term numerical weather forecast is the core of a national numerical forecast system. The method of the invention makes remarkable progress in the aspects of global mode dynamic frame stability and calculation precision, global mode physical process comprehensive improvement, mode space three-dimensional variation and assimilation system development and high-quality satellite observation data assimilation technology, and forms a GRAPES _ GFS2.0 version with the resolution of 0.25 degrees, 60 layers in the vertical direction and the forecast aging of 10 days; the method has the following advantages:

(1) the stability, the quality conservation and the calculation precision of the mode power frame are obviously improved. Aiming at the problems of low calculation precision, serious non-conservation of quality and unstable calculation caused by easy occurrence of calculation noise caused by a traditional non-central two-time-layer semi-implicit semi-Lagrange integral algorithm adopted at the beginning of GRAPES _ GFS design, 8 aspects of improvement are completed, including non-interpolation semi-Lagrange potential temperature vertical advection calculation, high-precision polar region filtering, effective terrain relief of calculation error of air pressure gradient force by introducing terrain filtering, high-precision conservation scalar advection algorithm, polar region processing technology, quality conservation correction, terrain filtering, w-damming technology for restraining calculation noise and stratospheric adaptive Rayleigh friction.

(2) A complete physical process suitable for global forecasting is developed, and organized involvement, momentum transfer and cloud-base maximum allowable mass flux based on local CFL conditions are considered for deep convection parameterization. A turbulent diffusion type shallow convection scheme is changed into a mass flux type consistent with deep convection, and the mass flux of the shallow convection cloud base is improved based on the surface buoyancy flux. Night stable boundary layer calculation based on a near-stratum stability function is changed into a local diffusion scheme, and a turbulence enhancement effect caused by laminated cloud top radiation cooling is increased. The small-scale terrain turbulent flow dragging, the blocking flow dragging and the damping caused by the low-level and high-level gravity wave breaking are developed, and the south wind deviation of the mode in the east Asia region is obviously improved. On the basis of an autonomously developed two-parameter cloud physical scheme, a macroscopic cloud and cloud amount forecasting scheme is developed, and the scale reduction of tropical lattice points and the deviation of cloud amount forecasting are obviously improved.

(3) A mode space three-dimensional variation assimilation system is developed, optimization is completed in the aspects of gradient calculation precision, balance constraint of mode surface balance equation solving and power-statistics combination, non-equidistant difference calculation of background temperature profiles and background error variance, and the stability and the calculation efficiency of an assimilation prediction circulating system are improved by reconstructing a digital filtering module.

(4) A set of mature high-quality data assimilation application technology is formed, a constrained satellite data deviation correction scheme, a control variable random disturbance method for bright temperature data quality control, variation quality control of exploration data and a set of mature methods for links from cloud detection, quality control, deviation correction, channel selection, sparseness to observation error covariance estimation of satellite data assimilation are developed, and the method and the system significantly contribute to high-quality application observation information, improvement of data utilization rate and improvement of analysis accuracy. At present, the amount of assimilated satellite data reaches 70%.

The method of the invention greatly improves the stability and the precision of the global medium-term numerical prediction system of GRAPES _ GFS, and the service version of L60 at 0.25 DEG and the original service T_L639L60 and ECMWF and NCEP (National Centers for environmental Prediction) comparison show that although there is a gap between the forecast potential field statistical test index and international fashion, it is more T_L639L60 has obvious improvement, rain belt prediction ability and prediction of high temperature process are close to or exceed ECMWF prediction level, and near-ground 2 m temperature prediction error is obviously better than T_L639L60, plays an important supporting role in the central meteorological station. A series of ideal tests verify that the PRM scheme has high precision, particularly has obvious advantages in a region with large water vapor gradient, small dispersion and dissipation errors and better conservation and conformality than the traditional half Lagrange advection scheme. The PRM scheme can effectively improve the simulation of the mode on the distribution of water substances, improves the forecasting effect of rainfall and has obvious effect on the improvement of the comprehensive forecasting performance of the mode. Meanwhile, the global mode of the coarse resolution and the wet physical process of the high-resolution mesoscale mode are obviously different, the interaction between the explicit cloud physics and convection parameterization needs to be considered in the global mode of the coarse resolution, and the reasonable calculation of condensation and evaporation under the condition of partial saturation in the grid needs to be considered, so that the global mode is called as a macro cloud scheme. On the basis, an explicit cloud forecasting scheme is further developed.

The constrained deviation correction method reduces the influence of the background deviation of the mode on the deviation correction of the satellite radiance data, removes the systematic deviation of the data, better utilizes the observed information, improves the analysis precision and improves the global mid-term forecast. The method overcomes the influence of systematic warm deviation of an ECMWF forecasting system in a stratosphere and deviation of atmospheric chemical mode on satellite data deviation correction, and improves the analysis accuracy and the forecasting technique of the ECMWF temperature field and ozone.

The method has the following advantages in the aspect of assimilation framework:

(1) the theoretical research and the technical realization of inseparable horizontal and vertical correlation are completed, and a foundation is laid for subsequently improving the frame precision;

(2) the system gradient calculation accuracy is obviously improved;

(3) researching and establishing a solution scheme of an equilibrium equation on a mode surface;

(4) a balance equation combining power and statistics is researched and realized;

(5) the non-equidistant difference is adopted, so that the calculation precision of the background temperature profile is obviously improved, and the problem of large background temperature profile error on the level of severe change of the mode vertical layering thickness is solved;

(6) updating the background error variance;

(7) the digital filtering module is reconstructed and optimized, so that the stability and the calculation efficiency of digital filtering are improved;

(8) A3D-Var integrated single-point test system is established.

The method provided by the invention is used for improving the existing framework aiming at the defects of the existing semi-implicit semi-Lagrange integration scheme in the aspects of computational efficiency and expandability in the aspect of large-scale parallel computation, and provides a solving algorithm with high efficiency and high performance simultaneously based on a semi-implicit integration format and a linearization system.

Drawings

FIG. 1 is a schematic diagram of the predicted mean deviation of humidity field in units of: g/kg. a, b are latitudinal average vertical sections of deviation between prediction fields of a QMSL scheme and a PRM scheme and FNL respectively;

fig. 2 is a graph illustrating the cloud average total cloud number of 5 months in 2013 predicted by the GRAPES _ GFS, wherein the upper graph: YOTC, middle panel: original scheme, the following figure: a new scheme is provided.

FIG. 3 is an analysis field of annual average northern hemisphere heightThe distribution of the root mean square error with height compared with the ERA-Interim reanalysis data is shown schematically, and the solid black lines and the dotted black lines represent T respectively_LResults in 2015 for 639 and NCEP FNL;

FIG. 4 is a schematic diagram of the comparison between the deviation correction amount of the AMSUA channel 9 and the conventional correction method by the constrained deviation correction scheme and the frequency distribution of O-B before and after correction;

FIG. 5 is a diagram of a directory structure of the GRAPES _ GFS Global intermediate forecast system;

FIG. 6 shows GRAPES _ GFS2.0 and T_L639 comprehensive score card diagram of pattern forecast effect, the score card is composed of ACC and RMSE averaged in representative hierarchical regions, each region is: EASI (east Asia region; 15 ℃ N-65 ℃ N,70 ℃ E-145 ℃ E), NH (northern hemisphere; 20 ℃ N-90 ℃ N), SH (southern hemisphere: 20 ℃ S-90 ℃ S), TRO (hot zone region: 20 ℃ S-20 ℃ N); the large triangles indicate that the change is obvious, the small triangles indicate that the change is obvious and pass significance tests, and the squares indicate that the change is not obvious;

FIG. 7 is a graph showing the average evolution characteristics (0-168 hours) of different forecast aging of 500hPa altitude field ACC; (a) northern hemisphere, (b) east asia;

FIG. 8 is a schematic diagram of the ACC time evolution characteristics of the 500hPa altitude field predicted by the northern hemisphere on the 5 th day;

figure 9 is a graphical representation of the probability distribution characteristics of the 500hPa height field ACC in the northern hemisphere predicted by day five,

FIG. 10 is a graph of GRAPES _ GFS2.0 and TL639 mode forecasted precipitation ETS (a) and BIAS (b) skill scores for various levels in the Chinese area.

Detailed Description

The method comprises the following steps:

wherein D_upRepresenting the lift-off rate of updraft in the cloud, M_upRepresenting the mass flux of the updraft,/_xRepresenting the content of cloud water or cloud ice in the ascending air flow in the cloud, q_xThe water content of the cloud water or cloud ice is the lattice point scale; wherein

Cloud micro-physics and macro cloud source-sink terms, respectively.

Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Fig. 1 shows the results of 8-day prediction test for one month continuously based on the initial value of NCEP _ FNL, which ranges from 7/month 1/12 in 2009 to 7/month 31/12. The figure shows the weftwise average profile of the deviation of the wet prediction field from the analysis field. It can be seen that when the QMSL scheme is used to deliver water substances, the model forecast middle-low humidity field is significantly wet compared to the NCEP _ fnl (NCEP final) analysis field, the average humidity of the lower layer of the northern hemisphere is greater than 6-8g/kg, and the trend of the wet is extended to more than 500hpa along with the movement of convection. The positive deviation of the humidity field is significantly mitigated when the PRM scheme is employed.

Fig. 2 shows the comparison test result of the cloud forecast performance of the cloud scheme which is developed by the GRAPES _ GFS using the protocloud micro-physical scheme WRF WSM6 and the cloud scheme which is developed autonomously with the YOTC, and the average total cloud amount of 5 months in 2013 is selected. As can be seen from fig. 2, the new cloud solution has a significant advantage over the original cloud solution in terms of cloud amount prediction, and the global distribution of the new cloud solution is closer to YOTC, particularly in low latitude regions of the equator, the cloud amount prediction method significantly improves the phenomenon that the cloud amount of the diagnosis solution is significantly less, the cloud amount of the original cloud solution in the region only reaches about 0.4-0.5, and the new cloud amount prediction solution can reach 0.8-0.9, which is consistent with YOTC.

As shown in fig. 3, the height field analysis error decreases year by year; in the aspect of data application, a set of mature methods of a constrained satellite data deviation correction scheme, a control variable random disturbance method of bright temperature data quality control, variation quality control of exploration data, and links from cloud detection, quality control, deviation correction, channel selection, sparseness to observation error covariance estimation in satellite data assimilation are developed.

FIG. 4 is a schematic diagram of the comparison between the deviation correction amount of the AMSUA channel 9 and the conventional correction method by the constrained deviation correction scheme and the frequency distribution of O-B before and after correction; wherein, (a) the frequency distribution before and after the AMSUA channel 4 is corrected by the constraint deviation correction scheme; (b) deviation of comparison between the GRAPES bright temperature and the FNL bright temperature; (c) correcting the correction amount of the AMSUA channel 9 by the traditional deviation correction; (d) correction of AMSUA channel 9 by CBC; (e) the difference between the two deviation correction amounts; the observation period is 6 months and 1 day to 10 days in 2013.

Fig. 5 is a table of contents structure of a GRAPES _ GFS global medium term numerical prediction system, which performs a 2-year assimilation prediction cycle test for a total of 2 years from 9 months in 2013 to 8 months in 2015 by using a GRAPES _ GFS2.0 version, and displays the prediction result from three aspects of isobaric surface element prediction, ground element prediction and multi-service center prediction skill comparison.

FIG. 6 shows GRAPES _ GFS2.0 and the current service T_L639 model is a comprehensive scoring card for two-year forecasting effect in four main assessment areas around the world. As can be seen, the indexes of the prediction performance of each item of GRAPES _ GFS2.0 are almost better than T_L639, only shows differences in temperature and altitude field in tropical and east asia areas in the hierarchy and aging; GRAPES _ GFS2.0 vs. T, especially for wind field forecasts_L639 there is a more significant improvement.

Fig. 7a and b show the average evolution characteristics of different forecast ages of 500hPa height field pitch flat Correlation coefficient (ACC) in the northern hemisphere (fig. 7a) and east asia (fig. 7b), respectively. It can be seen that GRAPES _ GFS2.0 is relative to T at different times, whether in east Asia or in the northern hemisphere_L639 all show significant improvement. The improvement on different spatial scales is further analyzed through wavelet decomposition, the improvement on each spatial scale is realized, and the improvement on the planet scale is more remarkable; from the aspect of forecasting root mean square error, the forecasting precision of GRAPES _ GFS2.0 is T higher_L639 has a clear progress; note that GRAPES _ GFS2.0 is relative to T in the southern hemisphere_L639 there is a more significant improvement.

Further consider the evolution characteristics of the 500hPa altitude field ACC of the northern hemisphere on the forecast day 5 for two consecutive years, as shown in FIG. 8, the stability and T of the forecast technique of GRAPES _ GFS2.0 day by day on day 5_L639 good consistency, and most cases ACC is higher than T_L639 average value 0.814 (T)_L639 at 0.801).

FIG. 9 shows the 500hPa altitude field ACC probability density distribution characteristic of the northern hemisphere predicted at day 5, as can be seen more clearly, compared to T_L639 mode, GRAPES _ GFS2.0 and 500hPa thereofThe high value area of ACC is more concentrated between 0.8-0.9, which shows that the stability of forecast skill is better.

FIG. 10 shows GRAPES _ GFS2.0 and T averaged over 2 years_L639 forecast fair risk Score (ETS) for precipitation and BIAS skill Score. As can be seen from the ETS score, relative to T_LIn the 639 mode, the GRAPES _ GFS2.0 shows a clear advantage in the forecast of light rain or rain zones, with a slight difference in the forecast of precipitation above the level of moderate rain, but the GRAPES _ GFS2.0 gradually shows an improved effect as the forecast age increases. As can be seen from the BIAS score, GRAPES _ GFS2.0 is overall lower than T_L639 mode, T is effectively improved_L639 null report too high a problem.

In addition, comprehensive statistical tests are also carried out on the wind field, the temperature field and the near-ground elements (2 m temperature and 10 m wind), and the patterns of the main numerical forecasting service centers of ECMWF and NCEP are compared and analyzed. The following conclusions can be drawn in combination with the above test results:

1. the forecast indexes of GRAPES _ GFS2.0 are all over the GRAPES _ GFS 1.0 version and T_L639 compared to the GraPES _ GFS2.0 isobaric surface element prediction, there is a clear advantage in the troposphere.

2. For precipitation forecasts, and T_L639 compared with the GRAPES _ GFS2.0, the method has the obvious advantages in the forecast of light rain, and the forecast of mid-rain and above precipitation shows a certain lagging trend, but the GRAPES _ GFS2.0 gradually shows an improved effect with the increase of forecast time. At the same time, T_LThe 639 mode null report is evident, while the GRAPES _ GFS2.0 advantage is evident.

3. GRAPES _ GFS2.0 vs. T in 2 m temperature error of near-ground element_L639 mode is improved significantly, and T is reduced effectively_L639 mode is in the high error value region of Qinghai-Tibet plateau. However, the 10-meter wind field forecast has a certain problem of slightly large error.

Compared with the ECMWF and the NCEP international main numerical prediction products, the GRAPES _ GFS2.0 situation field statistical test indexes are consistent in time evolution trend, but certain differences exist in magnitude. The rainfall raining forecasting capacity is close to the forecasting level of the ECMWF, the mode empty forecasting problem under different thresholds is restrained, and the rainfall forecasting situation of the China area, particularly the situation of the main rainfall area in the southeast China is closer to the actual situation.

The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the present disclosure should be covered within the scope of the present invention claimed in the appended claims.

Claims

1. The global medium term forecast (GRAPES _ GFS) is characterized by comprising the following steps of:

step 1, establishing a GRAPES _ GFS global middle-term numerical forecasting system, which comprises a data assimilation module, a dynamic framework module, a physical process module, a parallel computation module, a data preprocessing module and an operation script module;

step 2, adopting non-interpolation potential temperature Lagrange advection calculation, optimization of polar region filtering scheme, introduction of a scalar advection scheme with terrain filtering considering effective terrain and high-precision conservation, development of quality conservation correction, terrain filtering, w-damming and introduction of stratospheric Rayleigh friction effect to improve the stability, quality conservation and calculation precision of the mode power frame; the calculation precision of the mode wet physical quantity field is improved by adopting an independently developed conformal positive constant conservation semi-Lagrange scalar advection scheme and an polar region processing technology;

step 3, according to a semi-Lagrange mode and an Euler flux form equation set, conservation is achieved, and a scalar advection scheme PRM based on a segmented rational function is developed;

step 5, rolling-out feedback of large-scale macro cloud, deep convection and shallow convection and explicit cloud amount forecast are increased on the basis of a double-parameter cloud micro physical process to form a complete global mode cloud computing scheme;

q in the above equations (1), (2) and (3)_xWater contents representing water vapor and five water condensates of cloud water, rain water, cloud ice, snow and aragonite, N_xRepresenting the number concentration of other four particles except water vapor and cloud drops, and a represents the cloud amount; at present, only cloud water q is considered for the influence of the large-scale cloud condensation process and the deep and shallow convection rolling-out_cAnd Yunying ice q_iIgnoring the situation for snow q_sRain drops q_rAnd aragonite q_gAnd the effect of various particle number concentrations;

wherein δ x ═ x_a-x_b，x_aAnd x_bIs a column vector composed of mode variables, which respectively represent an analysis field and a background field; b is the error covariance matrix of the background field; h is an observation operator, which converts the mode variables into observation equivalent quantities; r is the observed error covariance matrix, d is the observed increment; the coordinate and the variable definition of the 3D-Var of the pattern space are completely matched with the GRAPES _ GFS forecast pattern, and the analysis variable is the same as the state variable of the pattern; converting the analysis variables into irrelevant control variables among the variables by adopting a variable transformation method so as to reduce the dimension of the background error covariance;

step 8, using the information of satellite radiance data calibration as prior constraint to deduce a deviation correction method with constraint on satellite radiance data;

2. The medium term numerical prediction global condition _ GFS as claimed in claim 1, wherein the step 3 comprises the steps of:

3. The medium term numerical prediction global condition _ GFS as claimed in claim 1, wherein the step 4 comprises the steps of:

4. The medium term numeric forecast (GRAPES _ GFS) as claimed in claim 1, wherein in said step 6, the influence term of the rollout process on the grid-scale cloud is:

Cloud micro-physics and macro cloud source-sink terms, respectively.