CN110211325A - A kind of area road icing high precision monitor early warning system based on meteorological big data - Google Patents
A kind of area road icing high precision monitor early warning system based on meteorological big data Download PDFInfo
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- G08B19/02—Alarm responsive to formation or anticipated formation of ice
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Abstract
The area road icing high precision monitor early warning system based on meteorological big data that the invention discloses a kind of, including hardware running environment is built, live data is collected and analysis, Mesoscale Numerical Forecast tool NONLINEAR CALCULATION, the linearity correction of temperature DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEM result, region Surface Temperature Retrieval and area road icing conditions early warning.The beneficial effects of the present invention are: introducing the data of meteorological satellite, the dimension of meteorological measuring is expanded, participates in the Data Assimilation of Numerical Prediction Models, improves the precision of forecast result indirectly；Numerical model DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEM is introduced, when gathering forecast result, using sliding training period, weight coefficient changes at any time, improves accuracy；The relationship of temperature, surface temperature, steam under different geographical environments is sought by linear analysis using a variety of representative stations, then from point to surface, improves the accuracy of area road icing conditions prediction.
Description
Technical Field
The invention relates to a monitoring and early warning system, in particular to a high-precision monitoring and early warning system for regional road icing based on meteorological big data, and belongs to the technical field of atmospheric detection and meteorological forecast.
Background
With the development of social economy and technological progress, transportation becomes an important life line of national economic production and people's social life, however, each link of transportation is directly influenced by meteorological conditions. According to data statistics, 50% of traffic accidents on roads in China, 71% of traffic accidents with serious traffic accidents and 65% of direct economic losses occur in severe weather caused by adverse weather conditions, and the road icing influence is particularly obvious in winter and early spring. When the road freezes, the friction between the wheels and the road surface is greatly weakened, so that the vehicle is easy to slip, and the vehicle cannot be stopped, thereby causing traffic accidents. Pedestrians are easy to slip and fall down to cause tumble injury, and the existing meteorological service aiming at road icing is to monitor the road icing by using an automatic meteorological station and then early warn according to a detection result; and secondly, estimating the possible road icing by using the temperature data in the weather forecast result.
However, for the existing meteorological services, one of the meteorological observations at present is single-point observation, and the road icing conditions in various geographic environments in a large area cannot be monitored, and although the ground surface temperature and the air temperature have a good correlation relationship, the correlation between the ground surface temperature and the air temperature is influenced by various factors. The change of the surface temperature is influenced by the property of the ground, the dry and wet conditions, and the air temperature is influenced by the surrounding environment. And thirdly, the road icing is related to the surface temperature and the moisture content, so that even if no precipitation occurs, the icing phenomenon is likely to occur, and the automatic meteorological station cannot realize the overall monitoring of the moisture content in a large area.
Disclosure of Invention
The invention aims to solve the problems and provide a high-precision monitoring and early-warning system for regional road icing based on meteorological big data.
The invention realizes the purpose through the following technical scheme: a high-precision monitoring and early-warning system for regional road icing based on meteorological big data comprises the following steps:
step 1, building a hardware operating environment, completing parallel operation through a server cluster technology of an Infiniband-based ultra-high-speed network, solving the problems of bandwidth and capacity of data interactive storage, and meeting the requirement of massive intensive operation required by meteorological data processing;
step 2, live data collection and analysis, namely acquiring real-time meteorological elements such as dry bulb temperature (air temperature), earth surface temperature, road icing, relative temperature, wind speed, precipitation, evaporation capacity, radiation flux, visibility and the like through an automatic meteorological station;
each station represents a type of area where roads are located, and comprises a shade surface or a sun surface of a mountain area, a place near rivers, lakes and seas, a plain and the like.
Step 3, performing nonlinear calculation by a mesoscale numerical forecasting tool (WRF), importing the meteorological observation data and data of an EC numerical forecasting mode of an European metaphase meteorological forecasting center (ECMWF) and data of a GFS numerical forecasting mode initial field and a boundary field of a national environment forecasting center (NECP) into a data pool, and then performing data homogenization by applying a three-dimensional variational algorithm (3D-Var);
supposing weather changesThe observed value of the quantity is O, the background value (such as the average value, the predicted value of the numerical mode and the like) of the variable is F, and the variance is sigmaOAnd σFThen, a weighted average is obtained:
a above is actually the minimum of the following valence functions J (S):
suppose there is a measurement S from a different method for the variable S1、S2，…，SNWith an error en＝SnS, assuming the error is random, unbiased<∈n>0 and obeying a normal distribution, the error of the nth observation falls in enAnd en+d∈nHas a probability of
Error variance:
for N observations:
is provided with
When I takes a minimum value, a maximum likelihood estimate is obtained.
From I to SaIs zero, resulting in:
the maximum likelihood estimate of S is a weighted average of Sn, the weight being the inverse of the error variance of each measurement.
When N is 2, the objective function is introduced:
wherein x is an analytical variable, xbIs the background field, xbIs an N-dimensional vector, yoIs the observed value, y is a value derived from the analysis variable, y ═ H (x), H is called the observation operator, y is the observed valueoY is an M-dimensional vector, B is a background error covariance, O is an observation error covariance, B is an N x N matrix, and O is an M x M matrix.
Mathematically, it can be shown that the best estimate (analysis field) of x is:
x＝xb+[B-1+HTO-1H]-1HTO-1(yo-Hxb)
it is the minimum point of the objective function, it is very difficult to directly solve x, the minimum point of J is generally found by using a descent algorithm, and the calculation gradient formula is as follows:
wherein,a tangent operator referred to as an observation operator.
Step 4, linearly correcting the air temperature set forecasting results, and firstly, accessing 6 groups of air temperature forecasting results and live detection data of the forecasting time; secondly, a mode of establishing a multiple linear regression model is adopted, and a time sequence is divided into a training period and a forecasting period; in the training period, 6 groups of multivariate linear regression models of forecast values and observation values are established, weight coefficients of 6 groups of forecast results are determined by multivariate linear regression analysis using the forecast values and the observation values, and 1 group of final forecast results in the forecasting period are obtained by the weight coefficients;
wherein, the multiple linear regression model
At a given grid point, for a certain meteorological element of a certain forecast age:
where O is the mean of the observations over the training period, aiIs the weight coefficient of the ith member of the participating set, FiAndthe prediction value of the ith mode and the prediction average value of the ith mode in the training period are respectively, and N is the total number of the modes participating in super ensemble prediction. Wherein the weight coefficient aiObtained from the minimization calculation of the error term G in the above training period equation:
where N is the number of time samples in the training period, St' and Ot' is the bias of the super set and observation field, respectively, of the training period, this equation operates on the format of each mode. The establishment of the multi-mode super ensemble prediction method relies on an error covariance matrix, which is the mode field deviation Fi' and Fj' set up, therefore, the weight coefficients for each mode are calculated from the error matrix C:
where N is the number of time samples in the training period, Fi' (t) and Fj' (t) are respectively the prediction values of the ith mode and the jth mode, and a linear algebraic equation is established.
Wherein,
oiis the observed value is flat, [ Ci，j]Is a matrix of [ a ]i]Is a matrix of n x n, and the matrix is a matrix of n x n,and solving for the n multiplied by 1 matrix by applying Gauss-Jordan elimination method to obtain a substitution formula.
Step 5, inverting the surface temperature of the region, substituting numerical prediction data of the air temperature into the linear relation in the step 2, and inverting the surface temperature result of the whole region;
and 6, early warning of the road icing condition of the region, comparing the numerical prediction data of the water vapor and the surface temperature result of the step 5 with the critical value interval of the step 2, predicting the road icing condition of the whole region, and early warning.
Preferably, in order to indirectly improve the accuracy of the forecast result, in step 2, the long-wave, short-wave and water vapor data of the area where the automatic weather station is located are obtained through the weather satellite, and the single-point water vapor data of the station position are separated.
Preferably, in order to obtain the latest forecast result through analysis and calculation on the real-time data, in step 2, the collected data is transmitted to the cloud server in real time in a wired or wireless manner.
Preferably, in order to improve the accuracy of the prediction, in step 4, the training period is a sliding training period, and the system continuously accesses subsequent live data and prediction results, and can perform comparison verification to obtain the latest weight coefficient, a numerical mode ensemble prediction is introduced, and when the prediction results are aggregated, the sliding training period is adopted, and the weight coefficient changes with time.
Preferably, in order to improve the accuracy of predicting the road icing condition of the area, in the step 4, the relationship between air temperature, surface temperature and water vapor in different geographic environments is searched through linear analysis.
The invention has the beneficial effects that: this regional road freezes high accuracy monitoring and early warning system based on meteorological big data reasonable in design:
(1) the data of the meteorological satellite is introduced, the dimensionality of meteorological observation data is enlarged, the data assimilation of a numerical forecasting mode is participated, and the precision of a forecasting result is indirectly improved;
(2) numerical mode ensemble prediction is introduced, when prediction results are aggregated, a sliding training period is adopted, weight coefficients change along with time, and accuracy is improved;
(3) the method utilizes various representative stations, finds the relationship among air temperature, surface temperature and water vapor in different geographic environments through linear analysis, and improves the accuracy of predicting the road icing condition of the region from point to surface.
Drawings
FIG. 1 is a schematic flow chart of the system of the present invention;
FIG. 2 is a schematic view of the linear relationship between the surface temperature and the air temperature according to the present invention;
FIG. 3 is a schematic view of the linear relationship between the surface temperature and the air temperature according to the present invention;
FIG. 4 is a schematic view of the linear relationship between the surface temperature and the air temperature according to the present invention;
FIG. 5 is a schematic view of a weather distribution according to an embodiment of the present invention;
FIG. 6 is a schematic illustration of an icing probability distribution in an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1 to 6, a high-precision monitoring and early warning system for regional road icing based on meteorological big data includes the following steps:
step 1, building a hardware operating environment, completing parallel operation through a server cluster technology of an Infiniband-based ultra-high-speed network, solving the problems of bandwidth and capacity of data interactive storage, and meeting the requirement of massive intensive operation required by meteorological data processing;
step 2, live data collection and analysis, acquiring real-time meteorological elements such as dry-bulb temperature (air temperature), earth surface temperature, road icing, relative temperature, wind speed, precipitation, evaporation capacity, radiation flux, visibility and the like through an automatic meteorological station, collecting live data of elements such as long wave, short wave and water vapor data of a meteorological satellite, air temperature, earth surface temperature, road icing state, relative humidity, precipitation, evaporation capacity, radiation flux and the like of the automatic meteorological station at a plurality of representative places, an initial field and an ambient field of a global numerical prediction mode (ECMWF) of a European middle-term meteorological prediction center, an initial field and an ambient field of a global numerical prediction mode (GFS) of a American environment prediction center, separating single-point water vapor data of the station positions, introducing data of the meteorological satellite, expanding dimensionality of meteorological observation data, participating in data assimilation of the numerical prediction mode, the precision of the forecast result is indirectly improved;
each station represents a type of area where roads are located, and comprises a shade surface or a sun surface of a mountain area, a place near rivers, lakes and seas, a plain and the like.
The collected data are transmitted to the cloud server in real time in a wired or wireless mode, so that the collected data can be rapidly transmitted, and the latest forecast result can be obtained through analysis and calculation of the real-time data.
Step 3, a mesoscale numerical prediction tool (WRF) performs nonlinear calculation, the meteorological observation data and the EC numerical prediction mode of the European metaphase meteorological prediction center (ECMWF) and the data of the GFS numerical prediction mode initial field and the boundary field of the American national environmental prediction center (NECP) are imported into a data pool, then data assimilation is performed by applying a three-dimensional variational algorithm (3D-Var), after the data assimilation is completed, 3 physical parameter schemes are selected, 6 groups of prediction results are calculated, and 1 group of results is calculated for each mode and each group of schemes;
assume that the observed value of the meteorological variable is O, the background value (e.g., mean, predicted value of numerical model, etc.) of the meteorological variable is F, and the variance is σOAnd σFThen, a weighted average is obtained:
a above is actually the minimum of the following valence functions J (S):
suppose there is a measurement S from a different method for the variable S1、S2，…，SNWith an error en＝SnS, assuming the error is random, unbiased<∈n>0 and obeying a normal distribution, the error of the nth observation falls in enAnd en+d∈nHas a probability of
Error variance:
for N observations:
is provided with
When I takes a minimum value, a maximum likelihood estimate is obtained.
From I to SaIs zero, resulting in:
the maximum likelihood estimate of S is a weighted average of Sn, the weight being the inverse of the error variance of each measurement.
When N is 2, the objective function is introduced:
wherein x is an analytical variable, xbIs the background field, xbIs an N-dimensional vector, yoIs the observed value, y is a value derived from the analysis variable, y ═ H (x), H is called the observation operator, y is the observed valueoY is an M-dimensional vector, B is a background error covariance, O is an observation error covariance, B is an N x N matrix, and O is an M x M matrix.
Mathematically, it can be shown that the best estimate (analysis field) of x is:
x＝xb+[B-1+HTO-1H]-1HTO-1(yo-Hxb)
it is the minimum point of the objective function, it is very difficult to directly solve x, the minimum point of J is generally found by using a descent algorithm, and the calculation gradient formula is as follows:
wherein,a tangent operator referred to as an observation operator.
Step 4, linearly correcting the air temperature set forecasting results, and firstly, accessing 6 groups of air temperature forecasting results and live detection data of the forecasting time; secondly, a mode of establishing a multiple linear regression model is adopted, and a time sequence is divided into a training period and a forecasting period; in the training period, 6 groups of multivariate linear regression models of forecast values and observation values are established, the relationship between air temperature, surface temperature and water vapor under different geographic environments is searched by using multivariate linear regression analysis of the forecast values and the observation values, the accuracy of regional road icing condition prediction is improved, the weight coefficients of the 6 groups of forecast results are ensured, 1 group of final forecast results in the forecast period are obtained by using the weight coefficients, the training period is a sliding training period, the system is continuously accessed to subsequent live data and forecast results, comparison and verification can be carried out, the latest weight coefficients are obtained, numerical mode ensemble prediction is introduced, when the forecast results are aggregated, the sliding training period is adopted, the weight coefficients change along with time, and the accuracy is improved;
wherein, the multiple linear regression model
At a given grid point, for a certain meteorological element of a certain forecast age:
where O is the mean of the observations over the training period, aiIs the weight coefficient of the ith member of the participating set, FiAndrespectively, the predicted value of the ith mode and the prediction average value of the ith mode in the training period, and N is the mode total participating in super ensemble predictionAnd (4) counting. Wherein the weight coefficient aiObtained from the minimization calculation of the error term G in the above training period equation:
where N is the number of time samples in the training period, St' and Ot' is the bias of the super set and observation field, respectively, of the training period, this equation operates on the format of each mode. The establishment of the multi-mode super ensemble prediction method relies on an error covariance matrix, which is the mode field deviation Fi' and Fj' set up, therefore, the weight coefficients for each mode are calculated from the error matrix C:
where N is the number of time samples in the training period, Fi' (t) and Fj' (t) are respectively the prediction values of the ith mode and the jth mode, and a linear algebraic equation is established.
Wherein,
oiis the observed value is flat, [ Ci，j]Is a matrix of [ a ]i]Is a matrix of n x n, and the matrix is a matrix of n x n,for an n x 1 matrix, applying Gand solving the auss-Jordan elimination method to obtain a substitution formula.
Step 5, inverting the surface temperature of the region, substituting numerical prediction data of the air temperature into the linear relation in the step 2, and inverting the surface temperature result of the whole region;
and 6, early warning of the road icing condition of the region, comparing the numerical prediction data of the water vapor and the surface temperature result of the step 5 with the critical value interval of the step 2, predicting the road icing condition of the whole region, and early warning.
The working principle is as follows: when the high-precision monitoring and early-warning system for regional road icing based on meteorological big data is used, the data of meteorological satellites are introduced, the dimensionality of meteorological observation data is enlarged, the data assimilation of a numerical forecasting mode is participated, and the precision of a forecasting result is indirectly improved; numerical mode ensemble prediction is introduced, when prediction results are aggregated, a sliding training period is adopted, weight coefficients change along with time, and accuracy is improved; the method utilizes various representative stations, finds the relationship among air temperature, surface temperature and water vapor in different geographic environments through linear analysis, and improves the accuracy of predicting the road icing condition of the region from point to surface.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (5)
1. The utility model provides an area road freezes high accuracy monitoring and early warning system based on meteorological big data which characterized in that: the method comprises the following steps:
step 1, building a hardware operating environment, completing parallel operation through a server cluster technology of an Infiniband-based ultra-high-speed network, solving the problems of bandwidth and capacity of data interactive storage, and meeting the requirement of massive intensive operation required by meteorological data processing;
step 2, live data collection and analysis, namely acquiring real-time meteorological elements such as dry bulb temperature, earth surface temperature, road icing, relative temperature, wind speed, precipitation, evaporation capacity, radiant flux, visibility and the like through an automatic meteorological station;
wherein each station represents a type of region where a road is located, and comprises a shade surface or a sun surface of a mountain area, a place near rivers, lakes and seas and a plain;
step 3, carrying out nonlinear calculation by a mesoscale numerical forecasting tool, importing the meteorological observation data and data of an EC numerical forecasting mode of a European metaphase meteorological forecasting center and data of a GFS numerical forecasting mode initial field and a boundary field of a national environment forecasting center into a data pool, and carrying out data homogenization by applying a three-dimensional variational algorithm;
assume that the observed value of the meteorological variable is O, the background value of the variable is F, and the variance is σOAnd σFThen, a weighted average is obtained:
a above is actually the minimum of the following valence functions J (S):
suppose there is a measurement S from a different method for the variable S1、S2，…，SNWith an error en＝SnS, assuming the error is random, unbiased<∈n>0 and obeying a normal distribution, the error of the nth observation falls in enAnd en+d∈nHas a probability of
Error variance:
for N observations:
is provided with
When the I takes a minimum value, obtaining a maximum likelihood estimation;
from I to SaIs zero, resulting in:
the maximum likelihood estimate of S is the weighted average of Sn, the weight being the inverse of the error variance of each measurement;
when N is 2, the objective function is introduced:
wherein x is an analytical variable, xbIs the background field, xbIs an N-dimensional vector, yoIs the observed value, y is a value derived from the analysis variable, y ═ H (x), H is called the observation operator, y is the observed valueoY is an M-dimensional vector, B is a background error covariance, O is an observation error covariance, B is an N x N matrix, and O is an M x M matrix.
Mathematically, it can be shown that the best estimate (analysis field) of x is:
x＝xb+[B-1+HTO-1H]-1HTO-1(yo-Hxb)
it is the minimum point of the objective function, it is very difficult to directly solve x, the minimum point of J is generally found by using a descent algorithm, and the calculation gradient formula is as follows:
wherein,a tangent operator referred to as an observation operator;
step 4, linearly correcting the air temperature set forecasting results, and firstly, accessing 6 groups of air temperature forecasting results and live detection data of the forecasting time; secondly, a mode of establishing a multiple linear regression model is adopted, and a time sequence is divided into a training period and a forecasting period; in the training period, 6 groups of multivariate linear regression models of forecast values and observation values are established, weight coefficients of 6 groups of forecast results are determined by multivariate linear regression analysis using the forecast values and the observation values, and 1 group of final forecast results in the forecasting period are obtained by the weight coefficients;
wherein, the multiple linear regression model
At a given grid point, for a certain meteorological element of a certain forecast age:
where O is the mean of the observations over the training period, aiIs the weight coefficient of the ith member of the participating set, FiAndthe prediction value of the ith mode and the prediction average value of the ith mode in the training period are respectively, and N is the total number of the modes participating in super ensemble prediction. Wherein the weight coefficient aiObtained from the minimization calculation of the error term G in the above training period equation:
where N is the number of time samples in the training period, St' and Ot' the deviations of the superset of training sessions and the observation field, respectively, this equation operates on the format of each mode; the establishment of the multi-mode super ensemble prediction method relies on an error covariance matrix, which is the mode field deviation Fi' and Fj' set up, therefore, the weight coefficients for each mode are calculated from the error matrix C:
where N is the number of time samples in the training period, Fi' (t) and Fj' (t) are respectively the prediction values of the ith mode and the jth mode are equal to each other, and a linear algebraic equation is established;
wherein,
oiis the observed value is flat, [ Ci，j]Is a matrix of [ a ]i]Is a matrix of n x n, and the matrix is a matrix of n x n,solving for an n multiplied by 1 matrix by applying a Gauss-Jordan elimination method to obtain a substitution formula;
step 5, inverting the surface temperature of the region, substituting numerical prediction data of the air temperature into the linear relation in the step 2, and inverting the surface temperature result of the whole region;
and 6, early warning of the road icing condition of the region, comparing the numerical prediction data of the water vapor and the surface temperature result of the step 5 with the critical value interval of the step 2, predicting the road icing condition of the whole region, and early warning.
2. The high-precision monitoring and early-warning system for regional road icing based on meteorological big data as claimed in claim 1, wherein: in the step 2, the long wave, the short wave and the water vapor data of the area where the automatic meteorological station is located are obtained through the meteorological satellite, the single-point water vapor data of the station position are separated, the data of the meteorological satellite are introduced, the dimensionality of meteorological observation data is enlarged, data assimilation in a numerical prediction mode is participated, and the precision of a prediction result is indirectly improved.
3. The high-precision monitoring and early-warning system for regional road icing based on meteorological big data as claimed in claim 1, wherein: in the step 2, the collected data is transmitted to the cloud server in real time in a wired or wireless mode, so that the collected data can be rapidly transmitted, and a latest forecast result can be obtained through analysis and calculation of the real-time data.
4. The high-precision monitoring and early-warning system for regional road icing based on meteorological big data as claimed in claim 1, wherein: in the step 4, the training period is a sliding training period, the system is continuously accessed to subsequent live data and forecast results, comparison and verification can be performed to obtain the latest weight coefficient, numerical mode ensemble forecasting is introduced, and when the forecast results are aggregated, the sliding training period is adopted, the weight coefficient changes along with time, and the accuracy is improved.
5. The high-precision monitoring and early-warning system for regional road icing based on meteorological big data as claimed in claim 1, wherein: in the step 4, the relationship among air temperature, surface temperature and water vapor in different geographic environments is searched through linear analysis, so that the accuracy of predicting the road icing condition of the region is improved.
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110599789A (en) * | 2019-09-17 | 2019-12-20 | 北京心中有数科技有限公司 | Road weather prediction method and device, electronic equipment and storage medium |
| CN110909447A (en) * | 2019-10-19 | 2020-03-24 | 中国电波传播研究所（中国电子科技集团公司第二十二研究所） | High-precision short-term prediction method for ionization layer region |
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| CN111986464A (en) * | 2020-07-15 | 2020-11-24 | 遵义同望智能科技有限公司 | System for forecasting road icing based on dynamic method |
| CN112147719A (en) * | 2020-09-29 | 2020-12-29 | 国家海洋环境预报中心 | Storm surge set data forecasting method and device based on GPU parallel computing |
| CN112147719B (en) * | 2020-09-29 | 2021-07-27 | 国家海洋环境预报中心 | Storm surge set numerical forecasting method and device based on GPU parallel computing |
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