CN113742927B - Meteorological forecast data quality detection method - Google Patents
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Abstract
The invention belongs to the technical field of weather service, and particularly relates to a method for detecting weather forecast data quality. The invention is a complete weather forecast data quality evaluation system, which starts from data receiving, then carries out quality control on observed data, then carries out fusion processing on multi-source heterogeneous weather data to form a standard unified data mode, adopts various evaluation indexes to evaluate the quality of forecast data at multiple angles, and finally analyzes sources and areas with poor quality of the forecast data. In the quality control stage of the observed data, correction is carried out by adopting the most typical method in meteorology and statistics; in a multi-source data fusion stage, fusing data by adopting a rapid space-time neighbor matching method aiming at each space-time resolution to form a uniform data format; in the quality evaluation stage, unlike the prior art, only a single evaluation index is adopted, the method not only adopts a regression evaluation standard with strong universality, but also introduces a meteorological evaluation index, so that the quality evaluation result is more accurate and reliable.
Description
Technical Field
The invention belongs to the technical field of weather service, and particularly relates to a method for detecting weather forecast data quality.
Background
With the rapid development of weather forecasting technology, besides the conventional weather map method combined with the mathematical statistics method, weather radar and satellite detection data are also applied to forecasting business, and meanwhile, the numerical forecasting and machine learning-based forecasting method emerges as a spring bamboo shoot after rain. However, weather forecast techniques are numerous and heterogeneous, and the quality of forecast data directly affects the effect of data application, so that necessary quality detection should be performed before using the weather forecast data.
In the aspect of meteorological data quality detection, most of the prior art aims at meteorological observation data, and the quality detection mainly examines the conditions of missing measurement, suspected error and error of the meteorological data. For the quality detection of weather forecast data, the feasibility and effectiveness of the forecast technology are verified by evaluating the quality of the forecast data based on a certain forecast technology, and the evaluation mode usually adopts regression evaluation indexes such as average absolute error (Mean Absolute Error, MAE) and root mean square error (Root Mean Squard Error, RMSE).
The existing weather forecast data quality detection technology is single in data source and simple in data structure, forecast results from various forecast technologies cannot be processed, and evaluation indexes of the weather forecast data quality detection technology are single.
Disclosure of Invention
According to the method for detecting the quality of the weather forecast data, the data fusion processing is carried out on the weather forecast data of multiple sources, and besides the regression evaluation standard with strong universality, the weather evaluation index is introduced, so that the quality evaluation result is more accurate.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a method for detecting the quality of weather forecast data, as shown in fig. 1, comprising the following steps:
s1, acquiring meteorological data comprising meteorological observation data and meteorological forecast data, wherein the meteorological data at least comprises positions, meteorological elements and time;
s2, performing data quality control on the acquired meteorological observation data, wherein the data quality control comprises the following steps of:
s21, missing value processing:
judging whether missing values exist in all the meteorological elements in a preset time period, if not, entering a step S22, if yes, counting the number of the missing values, filling by the average value of front and rear data when the number of the missing values is smaller than a set value and the front and rear data of the missing values are not empty, and filling by the average value of all the data of the meteorological elements in the preset time period when at least one of the front and rear data of the missing values is empty; when the number of the missing values is larger than or equal to a set value, eliminating the corresponding meteorological elements;
s22, abnormal value processing:
s221, checking a climatic limit value, and eliminating data violating climatic rules;
s222, extremum checking, namely eliminating data exceeding a set extremum in a certain time period in a certain area;
s223, 3 sigma principle inspection, calculating the average value of all data of all meteorological elements in the same space-time rangeAnd standard deviation->Wherein y is i For the meteorological observation data at time i, n is the effective sample size, the interval range is +.>The external data is regarded as abnormal value, and is removed;
s224, time-varying inspection, judging according to the space-time resolution of the received meteorological data, performing time-varying inspection within a preset time range (common time-varying inspection has 1 hour of time-varying and 3 hours of time-varying inspection), and eliminating data exceeding upper and lower time-varying limit values;
s3, multi-source meteorological data fusion: performing close matching on time and space on the acquired weather forecast data and the weather observation data processed in the step S2, setting uniform position identification on the matched data, and reserving data source information after fusion;
s4, constructing a weather forecast data quality evaluation system, and defining y i The weather observation data of the weather element at the moment i,for the forecast value of the weather element corresponding to the moment i, X i For the difference between meteorological observation data and forecast data, +.>For the mean value of the difference value, sigma is the standard deviation of the difference value, and the evaluation system comprises four evaluation modes:
1) Root Mean Square Error (RMSE): is defined as
2) Coarse difference rate: assume thatThe calculation mode of the coarse difference times is as follows: firstly, calculating sigma according to all data, checking contrast difference value one by one, if there is +.>When one of the maximum values is removed, a new sigma is calculated according to a formula for calculating standard deviation, if the difference value satisfies +.>Then, one of the maximum values is removed, then, a new sigma is calculated until no data needs to be removed, and the calculation formula of the coarse difference rate is as follows:
3) The consistency ratio is as follows: x is to be i <The data of 2σ are regarded as consistent, and the calculation formula is:the consistency ratio is measured by the consistency degree of the predicted value and the actual value;
4) Trending: according to the fusion data obtained in the step S3, the observed data and the forecast data in the same position identification area are respectively sequenced according to time, and the difference d between the time before and after is calculated i ＝y i+1 -y i Judging the lifting I of the corresponding meteorological element by adopting a symbolic function i ＝sgn(d i ) Wherein d is i ＞0，I i When d is =1 i ＜0，I i = -1, when d i ＝0，I i =0; thereby, each meteorological element in each region of the observation data and the forecast data generates a lifting sequence I Observation And I Forecasting When I Observation. I ＝I Forecast. I The primary trend is regarded as consistent,
s5, quality detection is carried out on weather forecast data:
according to the fusion data obtained in the step S3, the RMSE, the rough difference rate, the consistency rate and the trend consistency rate of each meteorological element are calculated according to the position identification and the division areas by utilizing the method in the step S4, the level threshold is set for 4 evaluation indexes in a unified mode, the index value of each element in each area is compared with the threshold, the quality level of the data is divided, and therefore the quality detection conclusion of the weather forecast data is obtained.
Compared with the prior art, the invention has the beneficial effects that the invention is a complete weather forecast data quality evaluation system, the quality control is carried out on the observed data from the beginning of data reception, then the fusion processing is carried out on the multi-source heterogeneous weather data to form a standard unified data mode, the quality of the forecast data is evaluated by adopting a plurality of evaluation indexes and multiple angles, and finally the sources and the areas with poor quality of the forecast data are analyzed. In the quality control stage of the observed data, correction is carried out by adopting the most typical method in meteorology and statistics; in a multi-source data fusion stage, fusing data by adopting a rapid space-time neighbor matching method aiming at each space-time resolution to form a uniform data format; in the quality evaluation stage, unlike the prior art, only a single evaluation index is adopted, the method not only adopts a regression evaluation standard with strong universality, but also introduces a meteorological evaluation index, so that the quality evaluation result is more accurate and reliable.
Drawings
FIG. 1 is a schematic diagram of a logic sequence according to the present invention;
Detailed Description
The following describes the scheme of the present invention in detail in connection with practical application environments:
the method mainly comprises the following steps:
s1, acquiring meteorological data comprising meteorological observation data and meteorological forecast data, wherein the meteorological data at least comprises positions, meteorological elements and time; the sources of the meteorological observation data comprise ground meteorological stations, satellites, radars, manual observation and the like, and the sources of the forecast data mainly comprise a meteorological office forecast center and a plurality of professional meteorological data service providers. The time resolution of the forecast data to be analyzed is in the order of hours to 15 days, and the spatial resolution is in the order of several kilometers to tens of kilometers. The data at least comprises position information, at least one piece of meteorological element information and forecast time.
S2, performing data quality control on the acquired meteorological observation data, wherein the data quality control comprises the following steps of:
s21, missing value processing:
judging whether missing values exist in all the meteorological elements in a preset time period, if not, entering a step S22, if yes, counting the number of the missing values, filling by the average value of front and rear data when the number of the missing values is smaller than a set value and the front and rear data of the missing values are not empty, and filling by the average value of all the data of the meteorological elements in the preset time period when at least one of the front and rear data of the missing values is empty; when the number of the missing values is greater than or equal to a set value, the corresponding meteorological elements are removed (namely, the corresponding meteorological elements do not participate in subsequent evaluation analysis);
s22, abnormal value processing:
s221, checking a climatic limit value, and eliminating data violating climatic rules;
s222, extremum checking, which is to reject data exceeding a set extremum (the occurrence probability is extremely small or impossible) occurring in a certain area within a certain time period;
s223, 3 sigma principle inspection, calculating the average value of all data of all meteorological elements in the same space-time rangeAnd standard deviation->Wherein y is i For the meteorological observation data at time i, n is the effective sample size, the interval range is +.>The external data is regarded as abnormal value, and is removed;
s224, time-varying inspection, judging according to the space-time resolution of the received meteorological data, performing time-varying inspection within a preset time range (common time-varying inspection has 1 hour of time-varying and 3 hours of time-varying inspection), and eliminating data exceeding upper and lower time-varying limit values;
s3, multi-source meteorological data fusion: performing close matching on time and space on the acquired weather forecast data and the weather observation data processed in the step S2, setting uniform position identification on the matched data, and reserving data source information after fusion; the method is used for carrying out neighbor matching on the multi-source meteorological data in time and space, and the matched data is provided with a unified position identifier. And (5) preserving data source information after fusion.
S4, constructing a weather forecast data quality evaluation system, and defining y i The weather observation data of the weather element at the moment i,for the forecast value of the weather element corresponding to the moment i, X i For the difference between meteorological observation data and forecast data, +.>For the mean value of the difference value, sigma is the standard deviation of the difference value, and the evaluation system comprises four evaluation modes:
1) Root Mean Square Error (RMSE): is defined asThe RMSE root mean square error is a commonly used measurement mode for measuring the deviation between a predicted value and an actual value;
2) Coarse difference rate: assume thatThe calculation mode of the coarse difference times is as follows: firstly, calculating sigma according to all data, checking contrast difference value one by one, if there is +.>When one of the maximum values is removed, a new sigma is calculated according to a formula for calculating standard deviation, if the difference is fullFoot->Then, one of the maximum values is removed, then, a new sigma is calculated until no data needs to be removed, and the calculation formula of the coarse difference rate is as follows: the coarse difference rate reflects the measurement of the deviation abnormal value between the predicted value and the actual value;
3) The consistency ratio is as follows: x is to be i <The data of 2σ are regarded as consistent, and the calculation formula is:the consistency ratio is measured by the consistency degree of the predicted value and the actual value;
4) Trending: according to the fusion data obtained in the step S3, the observed data and the forecast data in the same position identification area are respectively sequenced according to time, and the difference d between the time before and after is calculated i ＝y i+1 -y i Judging the lifting I of the corresponding meteorological element by adopting a symbolic function i ＝sgn(d i ) Wherein d is i ＞0，I i When d is =1 i ＜0，I i = -1, when d i ＝0，I i =0; thereby, each meteorological element in each region of the observation data and the forecast data generates a lifting sequence I Observation And I Forecasting When I Observation i ＝I Forecast. I The primary trend is regarded as consistent,
s5, quality detection is carried out on weather forecast data:
according to the fusion data obtained in the step S3, the RMSE, the rough difference rate, the consistency rate and the trend consistency rate of each meteorological element are calculated according to the position identification and the division areas by utilizing the method in the step S4, the level threshold is set for 4 evaluation indexes in a unified mode, the index value of each element in each area is compared with the threshold, the quality level of the data is divided, and therefore the quality detection conclusion of the weather forecast data is obtained. For example, the RMSE value can be considered to be close to the meteorological observation data within 10% of the data of each meteorological element in the observation data, the data quality is better, the data quality is considered to be medium if 10% -40%, the forecast data is considered to be inaccurate if more than 40%, the forecast value needs to be traced back to the source of the forecast technology, the difference between the forecast value and the observation value of each meteorological element at each time point is checked according to the region, and the influence of the related meteorological element needs to be considered if necessary; the level thresholds of the rough difference rate, the consistency rate and the trend consistency rate are set according to actual conditions.
The invention provides a method for detecting the quality of weather forecast data, which aims to evaluate the quality of the forecast data of each weather forecast technology, verify the effectiveness of the forecast technology, supervise the improvement and optimization of a forecast system and improve the accuracy of weather forecast.
The method can evaluate the quality of the multi-source forecast data simultaneously, and the multi-source heterogeneous and medium-small scale meteorological data are fused and divided into data with different time-space resolutions, so that regression evaluation indexes and meteorological evaluation indexes are adopted for the forecast data with different time-space resolutions, the multi-angle evaluation is carried out, and the data quality is evaluated more comprehensively.
In the prior art, quality detection is mostly aimed at meteorological observation data, and evaluation indexes are a missing measurement rate, a suspected error rate and an error rate; the invention has the advantages that the effect object is weather forecast data, and the quality of the forecast data is judged by comparing the difference between the forecast data and weather observation data.
In the prior art, the quality detection of weather forecast data is usually accompanied by the proposal of a new forecast technology, and the main value of the appearance is to verify the feasibility and effectiveness of the forecast technology, under the condition that the data source of the quality detection is single and the structure is simple, and the common evaluation modes are regression evaluation indexes such as MAE, RMSE and the like; the invention can receive and fuse weather forecast data generated by various forecast technologies by adopting a rapid space-time neighbor matching method, has rich and various evaluation indexes, not only has regression evaluation indexes, but also has weather indexes, and verifies the quality of the forecast data at multiple angles.
Claims (1)
1. A method for detecting the quality of weather forecast data, which is characterized by comprising the following steps:
s1, acquiring meteorological data comprising meteorological observation data and meteorological forecast data, wherein the meteorological data at least comprises positions, meteorological elements and time;
s2, performing data quality control on the acquired meteorological observation data, wherein the data quality control comprises the following steps of:
s21, missing value processing:
judging whether missing values exist in all the meteorological elements in a preset time period, if not, entering a step S22, if yes, counting the number of the missing values, filling by the average value of front and rear data when the number of the missing values is smaller than a set value and the front and rear data of the missing values are not empty, and filling by the average value of all the data of the meteorological elements in the preset time period when at least one of the front and rear data of the missing values is empty; when the number of the missing values is larger than or equal to a set value, eliminating the corresponding meteorological elements;
s22, abnormal value processing:
s221, checking a climatic limit value, and eliminating data violating climatic rules;
s222, extremum checking, namely eliminating data exceeding a set extremum in a certain time period in a certain area;
s223, 3 sigma principle inspection, calculating the average value of all data of all meteorological elements in the same space-time rangeAnd standard deviation->Wherein y is i For the meteorological observation data at time i, n is the effective sample size, the interval range is +.>The external data is regarded as abnormal value, and is removed;
s224, time-varying checking, namely judging according to the space-time resolution of the received meteorological data, performing time-varying checking within a preset time range, and eliminating data exceeding time-varying upper and lower limit values;
s3, multi-source meteorological data fusion: performing close matching on time and space on the acquired weather forecast data and the weather observation data processed in the step S2, setting uniform position identification on the matched data, and reserving data source information after fusion;
s4, constructing a weather forecast data quality evaluation system, and defining y i The weather observation data of the weather element at the moment i,for the forecast value of the weather element corresponding to the moment i, X i For the difference between meteorological observation data and forecast data, +.>For the mean value of the difference value, sigma is the standard deviation of the difference value, and the evaluation system comprises four evaluation modes:
1) Root mean square error RMSE: is defined as
2) Coarse difference rate: assume thatThe calculation mode of the coarse difference times is as follows: firstly, calculating sigma according to all data, checking contrast difference value one by one, if there is +.>When one of the maximum values is removed, a new sigma is calculated according to a formula for calculating standard deviation, if the difference value satisfies +.>Then, one of the maximum values is removed, then, a new sigma is calculated until no data needs to be removed, and the calculation formula of the coarse difference rate is as follows:
3) The consistency ratio is as follows: x is to be i The data for < 2σ are considered consistent, and the calculation formula is:
4) Trending: according to the fusion data obtained in the step S3, the observed data and the forecast data in the same position identification area are respectively sequenced according to time, and the difference d between the time before and after is calculated i ＝y i+1 -y i Judging the lifting I of the corresponding meteorological element by adopting a symbolic function i ＝sgn(d i ) Wherein d is i ＞0，I i When d is =1 i ＜0，I i = -1, when d i ＝0，I i =0; thereby, each meteorological element in each region of the observation data and the forecast data generates a lifting sequence I Observation And I Forecasting When I Observation. I ＝I Forecast. I The primary trend is regarded as consistent,
s5, quality detection is carried out on weather forecast data:
according to the fusion data obtained in the step S3, the RMSE, the rough difference rate, the consistency rate and the trend consistency rate of each meteorological element are calculated according to the position identification and the division areas by utilizing the method in the step S4, the level threshold is set for 4 evaluation indexes in a unified mode, the index value of each element in each area is compared with the threshold, the quality level of the data is divided, and therefore the quality detection conclusion of the weather forecast data is obtained.
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