CN104750953B - Medium and small-scale airborne substance atmospheric transport collective diffusion simulation method - Google Patents
Medium and small-scale airborne substance atmospheric transport collective diffusion simulation method Download PDFInfo
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Abstract
The invention relates to a medium and small-scale airborne substance atmospheric transport collective diffusion simulation method, which introduces a collective idea into atmospheric diffusion simulation, considers the essential diversity of various modes of medium and small scales and the fact that different meteorological numerical values are used for predicting results, integrates a collective analysis method and a collective diffusion simulation test method, and provides a set of multi-mode collective diffusion simulation results through comparison analysis with field experiments. The invention solves the uncertainty of single-mode simulation, and the integrated analysis result can provide a more comprehensive and comprehensive result, thereby meeting the requirements of medium and small-scale airborne substance atmospheric transport diffusion simulation.
Description
Technical Field
The invention relates to an atmospheric diffusion simulation technology, in particular to a medium and small-scale gas-carried substance atmospheric transport collective diffusion simulation method.
Background
In the past decade, the methods of ensemble forecasting of weather have been greatly developed, and ensemble forecasting has shown greater accuracy than a single numerical weather forecast. Similar benefits can also be derived from introducing this approach into atmospheric diffusion simulations. The atmospheric diffusion mode simulation comprises complex nonlinear processes, dynamic processes, thermodynamic processes, radiation, cloud accumulation micro-physics, turbulence and other processes. Different modes have different emphasis points, parameterization processing on certain processes is different, or initial boundary conditions are different, and the set diffusion simulation technology takes the characteristics of different modes into consideration.
In the case of emergency conditions and without monitoring data, there are two possibilities to consider the multi-model results simultaneously: allowing models to examine their primary differences allows decision makers to have more decisions based on simulation results as the situation progresses (e.g., based on extensive numerical forecasting). On this basis, the concept of ENSEMBLE (ENSEMBLE) is proposed and is constantly evolving and improving. It is a methodology that enables us to reduce the dependence on single model results. The aggregation method simultaneously analyzes a plurality of prediction results of the same event, and integrates all possible methods to obtain an aggregation result of atmospheric diffusion prediction. Different modes have different original mode characteristics, and all simulation results are considered for decision making. A large number of prediction results generated by different models are analyzed, and the results have complementarity, so that a more comprehensive evolution process and more possible basic decisions can be provided. The advantage of the aggregate mode in the field of emergency decision making is even more apparent when no monitoring data is available for evaluation.
At present, most of domestic collective modes are applied to the fields of weather forecast and air quality evaluation, foreign collective diffusion simulation is mainly used for large-scale atmospheric diffusion simulation research, and reports on the aspects of medium and small-scale airborne mass atmospheric transport collective diffusion simulation are not found.
Disclosure of Invention
Aiming at the existing atmospheric diffusion simulation technology, the invention aims to provide a medium and small-scale gas-carried substance atmospheric transport collective diffusion simulation method, which solves the uncertainty of single mode and determined parameter simulation by using the collective diffusion simulation technology and provides a more comprehensive and comprehensive diffusion simulation result.
The technical scheme of the invention is as follows: a middle and small-scale gas-carried substance atmospheric transport collective diffusion simulation method comprises the following steps:
(1) determining source item characteristics, a simulation range, simulation time and simulation precision according to a simulation object;
(2) determining a gas image field mode and a diffusion field mode, wherein the set members consist of the gas image field mode and the diffusion field mode;
(3) according to specific conditions, a meteorological field disturbance, turbulence disturbance and atmospheric diffusion physical process parameterization scheme is considered, and the influence of uncertain factors on a simulation result is analyzed;
(4) comparing and analyzing the simulation result with the field test observation data, and analyzing the simulation result and the field observation result inside the set member and the set member according to a set analysis method;
(5) and (4) according to the detection results of the average, dispersion, root mean square error, distance correlation coefficient and relative action characteristic curve of the sets among the set members, investigating the simulation effectiveness and accuracy of each set member.
Further, in the simulation method for the atmospheric transport collective diffusion of the medium and small-scale airborne substances, the meteorological field mode in the step (2) includes a quality conservation three-dimensional objective diagnosis wind field mode and a wind field forecasting mode.
Further, in the above-mentioned medium and small-scale airborne substance atmospheric transport collective diffusion simulation method, the diffusion field mode in the step (2) includes a gaussian diffusion mode, a particle random walk mode and a smoke cluster mode
Further, as described above, the atmospheric diffusion physical process parameterization scheme in the step (3) of the medium and small-scale airborne substance atmospheric transport collective diffusion simulation method includes a mixed layer height calculation method, an atmospheric stability calculation method, and a horizontal and vertical lagrange time scale calculation method.
Further, as described above, the collective diffusion simulation method for atmospheric transport of small and medium-sized airborne substances, the collective analysis method described in step (4) includes temporal analysis, spatial analysis and global analysis, wherein the temporal analysis includes: analyzing the probability of the smoke cloud arrival time and the change of the concentration time; the spatial analysis comprises: analyzing threshold cooperation level, percentage cooperation level and spatial characteristic parameters; the overall analysis includes predicting maximum trends.
The invention has the following beneficial effects: the invention analyzes a plurality of prediction results of the same event by using a set analysis method, and integrates all possible methods to obtain a set result of atmospheric diffusion prediction. Different modes have different mode characteristics and emphasis points, and all simulation results are considered for decision making. A large number of prediction results generated by different models are analyzed, and the results have complementarity, so that a more comprehensive atmospheric diffusion evolution process and more possible basic decisions can be provided. The advantages of the ensemble diffusion simulation technique in the field of emergency decision making are even more apparent when no monitoring data is available for evaluation.
Detailed Description
The present invention will be described in detail with reference to the following embodiments.
The invention provides a medium and small-scale gas-carried substance atmospheric transport collective diffusion simulation method, which comprises the following steps:
firstly, determining source item characteristics, simulation range, simulation time, simulation precision and the like according to a simulation object.
Determining a gas image field and a diffusion field mode, wherein the set members are composed of the gas image field mode and the atmospheric diffusion field mode and can be obtained by various methods, including: (1) single flow field mode and single diffusion mode; (2) multi-stream field mode and single diffusion mode; (3) single-flow field mode and multi-diffusion mode; (4) multi-streaming field mode and multi-diffusion mode.
The meteorological field mode comprises a quality conservation three-dimensional objective diagnosis wind field mode, a wind field forecasting mode and the like, all modes are calculated, and the obtained result is used as one of the set members; the diffusion field mode comprises a Gaussian diffusion mode, a particle random walk mode, a smoke cluster mode and the like, and all the modes are calculated, and the obtained result is used as one of the set members.
And thirdly, analyzing the influence of the various uncertain factors on the simulation result by considering a meteorological field disturbance, turbulence disturbance and atmospheric diffusion physical process parameterization scheme according to specific conditions.
If the influence of the uncertain factors on the diffusion simulation result needs to be considered, the influence can be considered respectively or uniformly; this step can be omitted if the above uncertainty factor need not be considered, but only the simulation result difference between the patterns is considered.
The perturbation of the meteorological field is mainly achieved by perturbing the meteorological field, for example, a random perturbation amount (e.g., a specified offset amount) is added in the horizontal (X, Y) and vertical (Z) directions respectively on the basis of the calculation result of the meteorological field.
Turbulent perturbation is achieved by introducing random perturbation factors into the diffusion model turbulence term.
The atmospheric diffusion physical process parameterization scheme comprises the following steps: a hybrid layer height calculation method, an atmospheric stability calculation method, a horizontal and vertical lagrange time scale calculation method, and the like.
And fourthly, comparing and analyzing with the field test observation data, and analyzing the inside of the set member, the simulation result of the set member and the field observation result according to a set analysis method, wherein the analysis comprises time analysis, space analysis and integral analysis. The time analysis comprises the following steps: analyzing the probability of the smoke cloud arrival time and the change of the concentration time; the spatial analysis comprises: analyzing threshold cooperation level, percentage cooperation level and spatial characteristic parameters; the overall analysis includes predicting maximum trends.
The main set analysis method is as follows:
(1) threshold synergy level (ATL)
Threshold synergy level (ATL) the spatial distribution consistency of several mode results can be summarized in a graph. ATL is defined as normalizing a given surface of the model (K =1, 2, … …, M), the predicted value being greater than a given threshold (CT, e.g. intervention level) for a given time:
wherein:
ATL: threshold synergy level, (%);
CT concentration threshold, (mg/m)3);
ck(x, y, t) concentration value forecast of certain set member k at (x, y) point and t moment, (mg/m)3);
δk: a judgment result is made, if ck≥cTTaking 1, otherwise, taking 0;
m: number of samples.
ATL is a prediction that is assumed to be based on the same weights, but may also be weighted and biased towards certain specific models. Studies have shown that ATL is a good indicator because it combines several in-model results in spatial analysis and in re-synergy gives an indicator of cloud spatial coverage.
(2) Percent synergistic level (APL)
Once the model synergy or confidence level is specified, the percent synergy level (APL) provides a particular representative variable. The prediction made by the M-set model with a position of (x, y) time t is represented by D (x, y, t) and is arranged in ascending order. The distribution percentage of the discrete values is:
Dp(x,y,t)=Di(x,y,t)+(0.01pM-i)*(Di+1(x,y,t)-Di(x,y,t)) (2)
wherein DiAnd Di+1Are two consecutive values in a series of classification predictions. The index i is such that it satisfies the following condition:
i≤0.01p≤i+1 (3)
wherein the content of the first and second substances,
p: a specified percent synergy level, (%);
APL is defined as the time t at which all positions (x, y) of the two-dimensional plane are obtained:
APL(x,y,t)=Dp(x,y,t) (4)
APL concentrates the results of several models in one expression, where the actual variable values are expressed and chosen by the level of restriction. Areas where, for example, high concentrations and quantitatively relevant reliability results are present can be determined by APL. For a given variable, time and given percentage values (e.g., 50% and 75%), the APL gives a distribution of variables that is consistent with the model-defined percentage values.
(3) Spatial feature parameter
Spatial signature comparison (FMS), defined as the percentage of the region where observed and predicted values overlap at important concentration levels. It is given as the intersection and union of the measured and predicted values areas.
Wherein:
FMS spatial feature parameter, (%);
m: the number of samples;
a (i, 1): whether an experimental measurement value exists in the point of interest i or not is considered, if so, 1 is taken, and if not, 0 is taken;
a (i, 2): and (4) regarding whether the concentration predicted value exists in the point i, if so, taking 1, and if not, taking 0.
FA2 and FA 5: the simulation results differed from the measurement results by a percentage of 2 times and 5 times.
FOEX: the simulation results are higher or lower than the percentage of the measured values.
(4) Aggregate predicted concentration ranges and corresponding probabilities (ENV, CCL)
Within the computation domain D, the spatial distribution of all modes that are greater than some threshold CT at a given time. ENV gives a slightly conserved region of predicted cloud coverage, within which the predicted concentration of at least one pattern is above a given threshold.
Wherein:
ENV (x, y, t): at a certain (x, y) point, whether the forecast concentration value of the set member is greater than a given threshold value exists at the time t, if so, 1 is selected, otherwise, 0 is selected;
d: calculating all point positions (x, y) of the domain;
cTgiven threshold concentration, (mg/m)3);
ck: the concentration value of some member k in the set is forecasted at the (x, y) point and the t moment, (mg/m)3)。
The probability density of a given pattern above a given threshold, given by the CCL, can be calculated on average and with different weights:
wherein the content of the first and second substances,
CCL (x, y, t): probability that the aggregate result is greater than some given threshold, (%);
m: the number of samples;
cTgiven threshold concentration, (mg/m)3);
ck(x, y, t): the concentration value of some member k in the set is forecasted at the (x, y) point and the t moment, (mg/m)3);
δk: a judgment result is made, if ck>cTGet 1, otherwise get 0.
Compared with the CCL, the ENL and the CCL can intuitively and effectively represent the uncertainty of the mode, and the complementary information of the two methods is clearer. The results given by ENV are somewhat conservative because it assumes that the predictions for all modes have the same probability of occurrence. The CCL can quantitatively evaluate the conformity of the patterns at different locations (ENV not shown), and these two parameters can also be used in a combination of aggregation systems.
(5) The smoke cloud arrival time is an important index of mode prediction and is also a key part of accident prediction, and the probability of the smoke cloud arrival time can be given by using a set technology.
Wherein the content of the first and second substances,
CTA (x, y, t) some (x, y) point, the cloud arrival probability at time t, (%);
m is the number of samples;
ck(x, y, t): the concentration value of some member k in the set is forecasted at the (x, y) point and the t moment, (mg/m)3);
δk: if the result is judged to be
If not, 0 is taken.
(6) Predicted maximum trend (MCT):
the maximum concentration trend at a fixed point can be represented by a time series of maxima in the point value for each mode in the set mode system, with MCT giving a more conservative result, as it represents the worst case in the set analysis.
MCT（x,y,t）=Max Ck(x,y,t) (9)
Wherein the content of the first and second substances,
MCT (x, y, t): predicted maximum concentration of members of the set at a certain point (x, y) and time t, (mg/m)3);
ck(x, y, t): the concentration value of some member k in the set is forecasted at the (x, y) point and the t moment, (mg/m)3)。
And fifthly, performing set inspection among the set members, and inspecting the simulation effectiveness and accuracy of each set member by using inspection methods such as set average, dispersion, root mean square error, distance correlation coefficient, relative action characteristic curve and the like.
The invention introduces the collective diffusion simulation technology into medium and small-scale transportation simulation in the material atmosphere, can avoid uncertain results caused by a single mode, comprehensively considers the influence of various meteorological and diffusion parameter disturbances on the simulation results, and improves the accuracy of mode simulation.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is intended to include such modifications and variations.
Claims (2)
1. A middle and small-scale gas-carried substance atmospheric transport collective diffusion simulation method comprises the following steps:
(1) determining source item characteristics, a simulation range, simulation time and simulation precision according to a simulation object;
(2) determining a gas image field mode and a diffusion field mode, wherein the set members consist of the gas image field mode and the diffusion field mode;
(3) according to specific conditions, a meteorological field disturbance, turbulence disturbance and atmospheric diffusion physical process parameterization scheme is considered, and the influence of uncertain factors on a simulation result is analyzed;
(4) comparing and analyzing the simulation result with the field test observation data, and analyzing the simulation result and the field observation result inside the set member and the set member according to a set analysis method;
(5) according to the detection results of the average, dispersion, root mean square error, distance correlation coefficient and relative action characteristic curve of the sets among the set members, the simulation effectiveness and accuracy of each set member are inspected;
wherein the meteorological field mode in the step (2) comprises a quality conservation three-dimensional objective diagnosis wind field mode and a wind field forecasting mode; the diffusion field mode in the step (2) comprises a Gaussian diffusion mode, a particle random walk mode and a smoke cluster mode;
the atmospheric diffusion physical process parameterization scheme in the step (3) comprises a mixed layer height calculation method, an atmospheric stability calculation method and a horizontal and vertical Lagrange time scale calculation method;
wherein the set analysis method described in step (4) comprises:
threshold synergy level ATL:
wherein
Wherein:
CT: threshold concentration (mg/m)3)
Ck(x, y, t) concentration value forecast of certain set member k at (x, y) point and t moment, (mg/m)3)；
Delta k is the judgment result, if Ck is more than or equal to CT, 1 is selected, otherwise 0 is selected;
m: the number of samples;
percent synergistic level APL:
APL(x,y,t)＝Dp(x,y,t) (4)
Dp(x,y,t)＝Di(x,y,t)+(0.01pM-i)*(Di+1(x,y,t)-Di(x,y,t)) (2)
where Di and Di +1 are two consecutive values in a series of classification predictions;
the index i is to satisfy the following condition
i≤0.01p≤i+1 (3)
Wherein P: a specified percent synergy level;
spatial signature parameter FMS, wherein:
wherein M: the number of samples;
a (i, 1): whether an experimental measurement value exists in the point of interest i or not is considered, if so, 1 is taken, and if not, 0 is taken;
a (i, 2): whether a concentration predicted value exists in the point of interest i or not is considered, if yes, 1 is taken, and if not, 0 is taken;
aggregating the predicted concentration ranges and the corresponding probabilities ENV and CCL, wherein:
wherein:
ENV (x, y, t): at a certain (x, y) point, whether the forecast concentration value of the set member is greater than a given threshold value exists at the time t, if so, 1 is selected, otherwise, 0 is selected;
d: calculating all point positions (x, y) of the domain;
CTgiven threshold concentration, (mg/m)3)；
Ck: the concentration value of some member k in the set is forecasted at the (x, y) point and the t moment, (mg/m)3)；
Wherein the content of the first and second substances,
CCL (x, y, t): probability that the aggregate result is greater than some given threshold, (%);
m: the number of samples;
CTgiven threshold concentration, (mg/m)3)；
CK(x, y, t): the concentration value of some member k in the set is forecasted at the (x, y) point and the t moment, (mg/m)3)；
δk: the result is determined as ck>cT is 1, otherwise 0 is selected;
cloud arrival time CTA:
wherein
CTA (x, y, t) some (x, y) point, the cloud arrival probability at time t, (%);
m is the number of samples;
Ck(x, y, t): the concentration value of some member k in the set is forecasted at the (x, y) point and the t moment, (mg/m)3)；
δk: judging a result; if so, taking 1; otherwise, 0 is selected;
prediction of maximum value trend MCT: wherein
MCT(x，y，t)＝Max Ck(x，y，t) (9)；
Wherein the content of the first and second substances,
MCT (x, y, t): predicted maximum concentration of members of the set at a certain point (x, y) and time t, (mg/m)3)；
Ck (x, y, t): the concentration value of some member k in the set is forecasted at the (x, y) point and the t moment, (mg/m)3)。
2. The medium and small-scale airborne substance atmospheric transport collective diffusion simulation method of claim 1, characterized in that: the set analysis method in step (4) includes temporal analysis, spatial analysis, and ensemble analysis, wherein the temporal analysis includes: analyzing the probability of the smoke cloud arrival time and the change of the concentration time; the spatial analysis comprises: analyzing threshold cooperation level, percentage cooperation level and spatial characteristic parameters; the overall analysis includes predicting maximum trends.
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| CN107526908A (en) * | 2016-06-21 | 2017-12-29 | 中国辐射防护研究院 | Lagrangian cigarette group Air Dispersion Modeling method in the evaluation of nuclear accident Off-Site consequence |
| CN107526910A (en) * | 2016-06-21 | 2017-12-29 | 中国辐射防护研究院 | A kind of wind field diagnostic method in nuclear facilities Accident Off-site Consequence evaluation |
| KR101802164B1 (en) * | 2016-09-20 | 2017-11-28 | 대한민국 | The automatic time-series analysis method and system for the simulated dispersal information of cloud seeding material |
| CN108808671A (en) * | 2018-07-03 | 2018-11-13 | 南京信息工程大学 | A kind of short-term wind speed DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEM method of wind power plant |
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