CN111443399A - Tropical cyclone strong wind circle identification system based on stepping frequency microwave radiometer data - Google Patents
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Abstract
The invention relates to a tropical cyclone strong wind circle identification system based on stepping frequency microwave radiometer data, which comprises: step (1): determining the SFMR observation tropical cyclone and date; step (2): for each observation tropical cyclone and date; the step (2) comprises the following steps in sequence: reading the optimal path data; reading the SFMR data; deleting the data of the take-off and landing process of the airplane; converting the data into a three-dimensional array of time, distance and observation; deleting low-quality data; obtaining a tropical cyclone wind speed change curve on the average of time and space; deleting low-quality data; identifying a seventh-level wind ring and a tenth-level wind ring; identifying a maximum wind speed radius; and (3): it is determined whether all tropical cyclones and dates have been calculated. The invention utilizes the observation data of the step frequency microwave radiometer, and can be effectively applied to the identification of the strong torrid zone cyclone by a series of data quality control. Not only lays a foundation for developing SFMR observation data in China, but also provides help for tropical cyclone structure recognition and disaster early warning.
Description
Technical Field
The invention relates to a tropical cyclone strong wind circle identification system, in particular to a tropical cyclone strong wind circle identification system based on stepping frequency microwave radiometer data, which applies SFMR data to the identification of the tropical cyclone strong wind circle for the first time, can not only effectively utilize the SFMR data, but also provide more accurate identification for a tropical cyclone field structure.
Background
Tropical cyclones (known as typhoons in the northwest pacific, hurricanes in the atlantic and eastern pacific, and cyclone storms in other sea areas) are one of the most damaging weather systems, and the strong winds, rainstorms, and secondary disasters that result often result in significant loss of lives and property. The strong windband of tropical cyclone can be accurately identified, which is helpful for understanding the structure and preventing disasters.
At present, the estimation of the tropical cyclone strong windband at sea in China is mainly estimated by a satellite radar or is obtained by limited observation of an offshore island reef meteorological station. The wind field estimated by the satellite is very large in error and obvious in underestimation phenomenon aiming at the environment with strong wind which is more than 30 m/s in a very short time like tropical cyclone and very severe sea condition. The observation of the foundation multi-mine radar can only accurately observe wind fields within 100 kilometers of the offshore area, and due to the limitation of the curvature of the earth, a large blind area exists in the observation of the wind fields in the near stratum. The relatively accurate offshore island observation data has the problems of non-uniform observation environment and observation quality, rare observation stations, observation in the offshore area of dozens of kilometers in the offshore area, and the like.
Under the current situation, international, particularly in the united states which are also frequently attacked by tropical cyclones, mainly rely on airborne observation to determine seven-level and ten-level wind rings according to lower threshold values of seven-level (13.9-17.1 m/s) and ten-level (24.5-28.4 m/s) of offshore tropical cyclones (particularly, open sea cyclones far from the coast) and combining tropical cyclone centers so as to better perform disaster prevention early warning, which becomes an important technology for international tropical cyclone forecast early warning, L andsea and Franklin (2013) research shows that uncertainty is obviously reduced when airborne observation is added to perform wind ring judgment.
The airborne observation mainly comprises GPS (global positioning system) downward projection exploration, airplane extrapolation and Step Frequency Microwave Radiometer (SFMR) data. The space distribution of the GPS downward projection air is sparse, the airplane extrapolation is often underestimated when the wind speed is strong, and the reliability of the SFMR data is relatively high in the strong wind environment of the tropical cyclone.
Based on this consideration, SFMR was added to the aircraft observation project in the united states since 1984, and a large amount of research data was obtained. The Shanghai typhoon research institute purchases the first domestic SFMR instrument and is about to be put into a scientific test in a typhoon field, but at present, no technology for identifying a strong wind circle of a tropical cyclone at sea based on SFMR data exists at home, and the application of the technology is helpful for filling the blank in the field.
Disclosure of Invention
In view of the above problems, the present invention provides a tropical cyclone strong wind circle identification system based on stepped frequency microwave radiometer data, which applies SFMR data to identify the tropical cyclone strong wind circle for the first time, not only can effectively utilize the SFMR data, but also can provide more accurate recognition for the structure of the tropical cyclone field.
The invention solves the technical problems through the following technical scheme: a tropical cyclone strong wind circle identification system based on stepping frequency microwave radiometer data comprises the following steps:
step (1): determining the SFMR observation tropical cyclone and date;
step (2): for each observation tropical cyclone and date;
step (201): reading Best path (Best track) data;
step (202): reading the SFMR data;
step (203): deleting the data of the take-off and landing process of the airplane;
a step (204): converting the data in the step (203) into a three-dimensional array of time, distance and observation;
step (205): deleting the low quality data in step (204);
step (206): obtaining a tropical cyclone wind speed change curve on the average of time and space;
step (207): deleting the low quality data in step (206);
a step (208): identifying a seventh-level wind ring and a tenth-level wind ring;
step (209): identifying a maximum wind speed radius;
and (3): it is determined whether all tropical cyclones and dates have been calculated.
In a specific embodiment of the present invention; the step (1) of determining the tropical cyclone and the date observed by the SFMR specifically comprises the following steps: and traversing the observation data folder, and determining the tropical cyclone and the observation date observed by all SFMRs according to the file names.
In a specific embodiment of the present invention; the reading of the optimal path data in step (201) specifically includes two situations: the first method comprises the following steps: if the tropical cyclone has a record in the best path data, reading all months, dates, hours, central longitude and latitude of the tropical cyclone and the maximum wind speed of the tropical cyclone at the recorded moment;
and the second method comprises the following steps: if the tropical cyclone or the moment is not recorded in the best path data, the intensity is considered to be too low or the data is wrong, and subsequent calculation is not carried out.
In a specific embodiment of the present invention; the reading of the SFMR data in step (202) specifically includes: reading all SFMR aircraft observations on the day, wherein the read variables comprise longitude, latitude, flight altitude and SFMR wind speed; the aircraft observation in the step adopts 1 or more aircrafts for observation.
In a specific embodiment of the present invention; the step (203) of deleting the data of the take-off and landing process of the airplane comprises the following steps:
deleting SFMR data in the processes of taking off and landing;
recognizing a takeoff process: the initial time of SFMR observation is considered as the rising starting time; searching from the initial time, wherein the first time meets the condition that the flying height is more than 2000m, more than 50% of flying points are in a non-rising stage (flying height less than or equal to the next second) in 20 minutes, and the flying points are regarded as rising end time;
and (3) identifying a landing process: the end time of the SFMR observation is regarded as the end time of the descent; searching forwards from the ending time, wherein the first time meets the condition that the flying height is more than 2000m, more than 50% of flying points in the previous 20 minutes are in a non-descending stage (the flying height is more than or equal to the flying height of the previous second), and the flying points are considered as descending starting time;
and deleting the data of the takeoff process from the beginning to the end of ascending and deleting the data of the landing process from the beginning to the end of descending for the observation data of each airplane, thereby finishing the first data quality control.
In a specific embodiment of the present invention; converting the data in the step (203) into a three-dimensional array of time, distance and observation in the step (204) specifically comprises: interpolating the optimal path data to obtain the longitude and latitude of the tropical cyclone center at the observation time, and calculating to obtain the distance between the optimal path data and the tropical cyclone center according to the longitude and latitude of the airplane at the observation time; finally, all data are converted into a three-dimensional array of observation time, distance from the center of the tropical cyclone, and SFMR wind speed.
In a specific embodiment of the present invention; the deleting of the low quality data in the step (204) in the step (205) specifically includes: utilizing the optimal path data interpolation to obtain the maximum tropical cyclone wind speed at the observation moment; the difference of the maximum wind speed of the cyclone of the heat zone from the observed wind speed of the SFMR is required to be not more than 10m/s within 0-150km from the center of the cyclone of the heat zone, 150-300km is not more than 5m/s, more than 300km is not more than 0, otherwise, a great error is considered to exist, and data is deleted; and traversing all the data to complete the second data quality control.
In a specific embodiment of the present invention; the step (206) of obtaining the tropical cyclone wind speed variation curve by space-time averaging specifically includes: the arrays were averaged spatially every 1km (0-600km, one average every 1 km), averaged temporally every 6 hours (6+/-3h, 12+/-3h, 18+/-3h, 24+/-3h, 30+/-3h, the portion exceeding 24 points being the next day), and finally the tropical cyclone wind speed level variation curves calculated according to the SFMR observation were obtained.
In a specific embodiment of the present invention; the deleting of the low quality data in the step (206) in the step (207) specifically includes: deleting missing data: if no observation exists in the array of 0-400km after the space-time average, deleting the array of data;
deleting error data: for the array after the space-time average, deleting data of which the wind speed change exceeds 10m/s per kilometer from two adjacent points; if the maximum value of the array wind speed after the time-space averaging appears beyond 200km, deleting the array data;
and traversing all the space-time average results to finish the third time of data quality control.
In a specific embodiment of the present invention; the step (208) of identifying the seven-level and ten-level wind rings specifically comprises the following steps: identifying a seven-grade wind ring and a ten-grade wind ring by using the wind speed after space-time averaging;
the wind speed of the seventh-level wind ring is more than or equal to 13.9m/s, and the wind speed of the tenth-level wind ring is more than or equal to 24.5 m/s;
setting the search range of a seven-level wind circle to be 80km-500km, setting the search range of a ten-level wind circle to be 20km-300km, and requiring that non-empty points of 100km at the outermost side of the search range must contain 50% of points smaller than seven/ten-level wind, otherwise, considering that data is wrong and outputting default; the specific search scheme is as follows: searching inwards from the outermost side of the search range, identifying the position of a first point as a seven/ten-level wind circle if the inner side of the first point is 20km, more than 50% of the points are not empty, the average wind speed is more than seven/ten-level wind, and more than 80% of non-default points exceed a seven/ten-level wind lowest threshold; if there is no point that satisfies the condition, a default is output.
In a specific embodiment of the present invention; and (209) identifying the radius of the maximum wind speed of the tropical cyclone in the horizontal direction by using the array after space-time averaging.
Setting a search range of 1-150km, and finding the maximum value as the maximum wind speed in the area with the maximum average value of 5 km. The maximum wind speed required to be found meets the following requirements: when the non-empty time is at 100-150km10 points or above, the maximum wind speed exceeds the average value of 100-150km for 5 m/s; when the non-space time is below 100-150km10 points, the maximum wind speed is greater than the average value of 0-100 km; if the condition is met, outputting the radius corresponding to the maximum wind speed as the maximum wind speed radius, and if the condition is not met, outputting a default.
In a specific embodiment of the present invention; the step (3) of determining whether all tropical cyclones and dates have been calculated specifically includes: if the calculation is not finished, calculating the next date or the next tropical cyclone, and if the calculation is finished, ending the program; if the calculation is not completed, the next date or the next tropical cyclone is calculated, and if the calculation is completed, the routine is ended.
The positive progress effects of the invention are as follows: the method for identifying the maximum wind speed radius of the typhoon based on the airborne data provided by the invention has the following advantages: the invention reads SFMR data and performs data quality control, converts the observation data passing the quality control into a three-dimensional array of observation time, distance from the center of the tropical cyclone and SFMR wind speed, obtains a horizontal variation curve of the tropical cyclone wind speed after space-time averaging, and further identifies the tropical cyclone strong wind circle. Not only lays a foundation for developing SFMR observation data in China, but also provides help for tropical cyclone structure recognition and disaster early warning.
Drawings
FIG. 1 is a schematic flow chart of a tropical cyclone strong wind circle identification system based on stepped frequency microwave radiometer data according to the present invention.
Fig. 2 is a schematic diagram of data errors of an airplane take-off and landing process by taking tropical cyclone Edouard 2014-year SFMR observation of 9-month-16-day.
Fig. 3 shows ten-and seven-level windband identification results of space-time average SFMR observation wind speed 3 hours before and after 9, 16, 12 (coordinated universal time, UTC) of tropical cyclone Edouard 2014. Wherein the scatter point is the space-time average SFMR wind speed, the thin solid line and the thin dotted line respectively represent a ten-level wind threshold and a seven-level wind threshold, and the thick solid line and the thick dotted line respectively represent a ten-level wind ring and a seven-level wind ring which are identified.
Fig. 4 is a probability distribution curve of ten-and seven-grade windband radii identified by all SFMR observations in the atlantic in 1998-2018, wherein the solid line represents the ten-grade windband and the dotted line represents the seven-grade windband.
FIG. 5 is a box plot of ten-level windband radii classified by observation time as identified by all SFMR observations in the Atlantic year 1998-.
FIG. 6 is a box diagram of the seven-class windcircle radius identified by all SFMR observations in the Atlantic in 1998-2018, classified according to observation time, wherein the symbols of the box diagram are as shown in FIG. 5.
FIG. 7 is a maximum wind speed radius probability distribution curve identified by all SFMR observations in the Atlantic in 1998 and 2018.
FIG. 8 is a box plot of the maximum wind speed radii identified by hurricane intensity for all SFMR observations in the Atlantic of 1998 and 2018, with the box plots labeled as FIG. 5. Furthermore, the abscissa is the hurricane intensity classification in the best track record and its corresponding data number: TD (thermoplastic suppression) represents a tropical low pressure with a maximum wind speed of less than 33m/s, and cat1-5 represents that a wind speed v satisfies 33 m/s-v < 43m/s,43 m/s-v < 50m/s,50 m/s-v < 58m/s,58 m/s-v < 70m/s, and 70m/s, respectively, of a first-to fifth-stage hurricane.
FIG. 9 is a plot of the radius and latitude of ten-level windcircles identified by all SFMR observations in the Atlantic year 1998-2018.
Detailed Description
The following provides a detailed description of the preferred embodiments of the present invention with reference to the accompanying drawings.
The invention reads SFMR data and performs data quality control, converts the observation data passing the quality control into a three-dimensional array of observation time, distance from the center of the tropical cyclone and SFMR wind speed, obtains a horizontal variation curve of the tropical cyclone wind speed after space-time averaging, and further identifies the tropical cyclone strong wind circle.
The method comprises the following specific steps:
step (1): determination of SFMR observations tropical cyclones and date: and traversing the observation data folder, and determining the tropical cyclone and the observation date observed by all SFMRs according to the file names.
Step (2): for each observation tropical cyclone and date:
step (201): read Best path (Best track) data:
the current Best Track data is from the United states national hurricane center (https:// www.nhc.noaa.gov/data/# hurdat). If the day of the tropical cyclone has a record in the Best Track data, reading all months, days, hours, longitude and latitude of the center of the tropical cyclone and the maximum wind speed of the recorded moment of the tropical cyclone. If the tropical cyclone or the moment is not recorded in the Best Track data, the intensity is considered to be too low or the data is wrong, and subsequent calculation is not carried out. When the method is used in coastal typhoon in China, the data of the best path of the typhoon of the China weather service bureau can be used as the reference.
Step (202): reading SFMR data: reading all SFMR aircraft observations (possibly more than 1 aircraft for observation) on the day, wherein the read variables comprise longitude, latitude, flight altitude and SFMR wind speed;
step (203): deleting the data of the take-off and landing process of the airplane: the SFMR data during take-off and landing have large errors and should be deleted.
Recognizing a takeoff process: the initial time of SFMR observation is considered as the rise start time. The initial time is searched, the first time satisfies that the flying height is more than 2000m, and more than 50% of flying points are in a non-ascending stage (flying height less than or equal to the next second) after 20 minutes, and the flying points are regarded as ascending end time.
And (3) identifying a landing process: the end time of the SFMR observation is regarded as the end time of the descent. When the search is carried out from the end time to the front, the first time meets the condition that the flying height is more than 2000m, and more than 50% of flying points in the previous 20 minutes are in a non-descending stage (the flying height is more than or equal to the flying height of the previous second), and the flying points are considered as descending start time.
And deleting the data of the takeoff process from the beginning to the end of ascending and deleting the data of the landing process from the beginning to the end of descending for the observation data of each airplane, thereby finishing the first data quality control.
A step (204): converting the data into a three-dimensional array of time, distance and observation: interpolation is carried out on Best Track data to obtain the longitude and latitude of the tropical cyclone center at the observation moment, and then the distance between the Best Track data and the tropical cyclone center can be calculated and obtained according to the longitude and latitude of the airplane at the observation moment. Finally, all data can be converted into a three-dimensional array of the observation time, distance from the center of the tropical cyclone, SFMR wind speed.
Step (205): deleting low quality data: and (4) utilizing Best Track data interpolation to obtain the maximum tropical cyclone wind speed at the observation moment. The difference of the maximum wind speed of the cyclone of the heat zone from the observed wind speed of the SFMR is required to be not more than 10m/s within 0-150km from the center of the cyclone of the heat zone, 150-300km is not more than 5m/s, more than 300km is not more than 0, otherwise, a large error is considered to exist, and data is deleted. And traversing all the data to complete the second data quality control.
Step (206): obtaining a tropical cyclone wind speed change curve on the average of space and time: the arrays were averaged spatially every 1km (0-600km, one average every 1 km), averaged temporally every 6 hours (6+/-3h, 12+/-3h, 18+/-3h, 24+/-3h, 30+/-3h, the portion exceeding 24 points being the next day), and finally the tropical cyclone wind speed level variation curves calculated according to the SFMR observation were obtained.
Step (207): deleting low quality data: deleting missing data: if no observation exists in the array of 0-400km after the space-time average, deleting the array of data.
Deleting error data: and deleting data of wind speed change more than 10m/s per kilometer from two adjacent points for the array after space-time averaging. If the maximum value of the wind speed of the array after the time-space averaging appears beyond 200km, the data of the array is deleted.
And traversing all the space-time average results to finish the third time of data quality control.
A step (208): identifying a seven-grade and a ten-grade wind ring: and identifying a seventh-level air ring (more than or equal to 13.9m/s) and a tenth-level air ring (more than or equal to 24.5m/s) by utilizing the air speed after space-time averaging.
Setting the search range of a seven-level wind circle to be 80km-500km, setting the search range of a ten-level wind circle to be 20km-300km, and requiring that non-empty points of 100km at the outermost side of the search range must contain 50% of points smaller than seven/ten-level wind, otherwise, considering that data is wrong, and outputting default. The specific search scheme is as follows: searching inwards from the outermost side of the search range, identifying the position of a point as a seven/ten-level wind circle if the inner side of the first point is 20km, the point is not empty and has more than 50%, the average wind speed is more than seven/ten-level wind, and more than 80% of non-default points exceed the lowest threshold of seven/ten-level wind. If there is no point that satisfies the condition, a default is output.
Step (209): identifying the maximum wind speed radius: and identifying the radius of the maximum wind speed of the tropical cyclone in the horizontal direction by using the array after space-time averaging.
Setting a search range of 1-150km, and finding the maximum value as the maximum wind speed in the area with the maximum average value of 5 km. The maximum wind speed required to be found meets the following requirements: when the non-empty time is at 100-150km10 points or above, the maximum wind speed exceeds the average value of 100-150km for 5 m/s; when the wind speed is not empty below 100-150km10 points, the maximum wind speed is larger than the average value of 0-100 km. If the condition is met, outputting the radius corresponding to the maximum wind speed as the maximum wind speed radius, and if the condition is not met, outputting a default.
And (3): judging whether all tropical cyclones and dates are calculated; if the calculation is not completed, the next date or the next tropical cyclone is calculated, and if the calculation is completed, the routine is ended.
The following is a specific example of implementation: FIG. 1 is a schematic flow chart of a tropical cyclone strong wind circle identification system based on stepped frequency microwave radiometer data according to the present invention. Referring to fig. 1, a specific embodiment of the present invention is as follows:
the system was applied to all SFMR tropical cyclone observations in the atlantic in 1998 and 2018:
step (1): determination of SFMR observations tropical cyclones and date:
traversing all data folders, and determining all tropical cyclone cases with SFMR observation and observation dates according to file names; in the sample test, all atlantic tropical cyclone cases in 1998 and 2018 were used.
Step (2): for each observation tropical cyclone and date:
step (201): read Best path (Best track) data:
the Best Track data is from the Atlantic Best path dataset (HURDAT2, https:// www.nhc.noaa.gov/data/HURDAT/HURDAT 2-1851-. If the tropical cyclone or the moment is not recorded in the Best Track data, the intensity is considered to be too low or the data is wrong, and subsequent calculation is not carried out.
Step (202): reading SFMR data: all SFMR aircraft observations (perhaps more than 1 aircraft for observation) were read for that day, with the variables read including longitude, latitude, altitude, SFMR wind speed.
Step (203): deleting the data of the take-off and landing process of the airplane: fig. 2 is a schematic diagram of data errors of an airplane taking off and landing process by taking tropical cyclone Edouard 2014 as an example, wherein the error data with the farthest distance is the taking off and landing process. It can be seen that the SFMR data has a large error during the take-off and landing and should be deleted.
Recognizing a takeoff process: the initial time of SFMR observation is considered as the rise start time. The initial time is searched, the first time satisfies that the flying height is more than 2000m, and more than 50% of flying points are in a non-ascending stage (flying height less than or equal to the next second) after 20 minutes, and the flying points are regarded as ascending end time.
And (3) identifying a landing process: the end time of the SFMR observation is regarded as the end time of the descent. When the search is carried out from the end time to the front, the first time meets the condition that the flying height is more than 2000m, and more than 50% of flying points in the previous 20 minutes are in a non-descending stage (the flying height is more than or equal to the flying height of the previous second), and the flying points are considered as descending start time.
And deleting the data of the takeoff process from the beginning to the end of ascending and deleting the data of the landing process from the beginning to the end of descending for the observation data of each airplane, thereby finishing the first data quality control.
A step (204): converting the data into a three-dimensional array of time, distance and observation:
interpolation is carried out on Best Track data to obtain the longitude and latitude of the tropical cyclone center at the observation moment, and then the distance between the Best Track data and the tropical cyclone center can be calculated and obtained according to the longitude and latitude of the airplane at the observation moment. Finally, all data can be converted into a three-dimensional array of the observation time, distance from the center of the tropical cyclone, SFMR wind speed.
Step (205): deleting low quality data: and (4) utilizing Best Track data interpolation to obtain the maximum tropical cyclone wind speed at the observation moment. The difference of the maximum wind speed of the cyclone of the heat zone from the observed wind speed of the SFMR is required to be not more than 10m/s within 0-150km from the center of the cyclone of the heat zone, 150-300km is not more than 5m/s, more than 300km is not more than 0, otherwise, a large error is considered to exist, and data is deleted. And traversing all the data to complete the second data quality control.
Step (206): obtaining a tropical cyclone wind speed change curve on the average of space and time: the arrays were averaged spatially every 1km point (0-600km, one average every 1 km), averaged temporally every 6 hours (6+/-3h, 12+/-3h, 18+/-3h, 24+/-3h, 30+/-3h, the portion exceeding 24 points being the next day), and finally the tropical cyclone wind speed level variation curves calculated according to the SFMR observation were obtained.
Step (207): deleting low quality data: deleting missing data: if no observation exists in the array of 0-400km after the space-time average, deleting the array of data.
Deleting error data: and deleting data of wind speed change more than 10m/s per kilometer from two adjacent points for the array after space-time averaging. If the maximum value of the wind speed of the array after the time-space averaging appears beyond 200km, the data of the array is deleted.
And traversing the space-time average result to finish the third data quality control.
A step (208): identifying a seven-grade and a ten-grade wind ring: and identifying a seventh-level air ring (more than or equal to 13.9m/s) and a tenth-level air ring (more than or equal to 24.5m/s) by utilizing the air speed after space-time averaging.
Setting the search range of a seven-level wind circle to be 80km-500km, setting the search range of a ten-level wind circle to be 20km-300km, and requiring that non-empty points of 100km at the outermost side of the search range must contain 50% of points smaller than seven/ten-level wind, otherwise, considering that data is wrong, and outputting default. The specific search scheme is as follows: searching inwards from the outermost side of the search range, identifying the position of a point as a seven/ten-level wind circle if the inner side of the first point is 20km, the point is not empty and has more than 50%, the average wind speed is more than seven/ten-level wind, and more than 80% of non-default points exceed the lowest threshold of seven/ten-level wind. If there is no point that satisfies the condition, a default is output.
Fig. 3 is a strong wind circle recognition result of space-time average SFMR observation wind speed 3 hours before and after 9, 16, 12 (coordinated universal time, UTC) of tropical cyclone Edouard 2014. Wherein the scatter point is the space-time average SFMR wind speed, the thin solid line and the thin dotted line respectively represent a ten-level wind threshold and a seven-level wind threshold, and the thick solid line and the thick dotted line respectively represent a ten-level wind ring and a seven-level wind ring which are identified.
It can be seen that the recognition effect of the recognition system is consistent with that of manual judgment, and the recognition system is reliable. In addition, the observed wind speed after space-time averaging is similar to the maximum wind speed (105knot, which is equal to 54m/s) recorded by Best Track when tropical cyclone occurs, and the result of space-time averaging is also accurate.
Step (209): identifying the maximum wind speed radius: and identifying the radius of the maximum wind speed of the tropical cyclone in the horizontal direction by using the array after space-time averaging.
Setting a search range of 1-150km, and finding the maximum value as the maximum wind speed in the area with the maximum average value of 5 km. The maximum wind speed required to be found meets the following requirements: when the non-empty time is at 100-150km10 points or above, the maximum wind speed exceeds the average value of 100-150km for 5 m/s; when the wind speed is not empty below 100-150km10 points, the maximum wind speed is larger than the average value of 0-100 km. If the condition is met, outputting the radius corresponding to the maximum wind speed as the maximum wind speed radius, and if the condition is not met, outputting a default.
And (3): determine if all tropical cyclones and dates have been calculated:
if the calculation is not completed, the next date or the next tropical cyclone is calculated, and if the calculation is completed, the routine is ended.
Finally, by utilizing all SFMR observation of the Atlantic in 1998 and 2018 and the tropical cyclone strong wind circle identification system based on the stepping frequency microwave radiometer data, 339 wind circles of ten grades, 629 wind circles of seven grades and 568 maximum wind speed radiuses are identified.
Fig. 4 is a probability distribution curve of ten-grade wind and seven-grade wind radius identified by all SFMR observations in the atlantic in 1998-2018, wherein a solid line represents a ten-grade wind circle and a dotted line represents a seven-grade wind circle. It can be seen that the ten-level and seven-level windbands basically accord with normal distribution and accord with natural law. Wherein, the maximum probability of the ten-level wind circle is about 100km, the data is concentrated, the maximum probability of the seven-level wind circle is about 250km, the data is dispersed, and the data is consistent with meteorological cognition.
FIGS. 5-6 are box plots of ten-and seven-level windband radius distributions, respectively, identified by 1998-2018-Atlantic SFMR observations, sorted by observation time, with the plus sign indicating an outlier. It can be seen that the outliers are few (ten-level: 1.5%; seven-level: 0.2%), and the ten-level and seven-level windband identifications are reliable.
FIG. 7 is a maximum wind speed radius probability distribution curve identified by all SFMR observations in the Atlantic in 1998 and 2018. It can be seen that the maximum wind speed radius probability maximum is about 35km, and the probability distribution curve is similar to normal distribution and is consistent with meteorological knowledge.
FIG. 8 is a box plot of maximum wind speed radii as classified by hurricane intensity as identified by all SFMR observations in the Atlantic of 1998 and 2018, where the plus sign indicates the outlier, the horizontal lines on the upper and lower extension lines of the box indicate the maximum and minimum values of non-outlier data, the upper and lower horizontal lines of the box indicate the upper and lower quartiles of non-outlier data, and the horizontal line in the center of the box indicates the median of the non-outlier data. The abscissa is hurricane intensity classification and its corresponding data number in best track records: TD (tropicalDepression) represents the tropical low pressure with the maximum wind speed less than 33m/s, cat1-5 represents that the wind speed v satisfies 33m/s and v < 43m/s,43m/s and v < 50m/s,50m/s and v < 58m/s,58m/s and v < 70m/s and first-to fifth-stage hurricanes with the wind speed v being equal to or less than 33m/s and v < 43m/s, respectively.
It can be seen that the maximum wind speed radius tends to decrease as the intensity of the tropical cyclone increases and tends to concentrate. And the identification result abnormal value is less (2.3%), and the identification result is also credible.
The invention is not only applied to tropical cyclone early warning service, but also has certain scientific research prospect.
FIG. 9 is a plot of the radius and latitude of ten-level windcircles identified by all SFMR observations in the Atlantic year 1998-2018. It can be seen that there may be a positive correlation between the ten-level wind circle radius and the latitude, which is related to the climate statistical rule of the tropical cyclone scale and the latitude of Knaffet al (2015), and has a good research value.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are merely illustrative of the principles of the present invention, but that various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined by the appended claims and their equivalents.
Claims (12)
1. A tropical cyclone strong wind circle identification system based on stepping frequency microwave radiometer data is characterized in that: the tropical cyclone strong wind circle identification system based on the stepping frequency microwave radiometer data comprises the following steps:
step (1): determining the SFMR observation tropical cyclone and date;
step (2): for each observation tropical cyclone and date;
step (201): reading Best path (Best track) data;
step (202): reading the SFMR data;
step (203): deleting the data of the take-off and landing process of the airplane;
a step (204): converting the data in the step (203) into a three-dimensional array of time, distance and observation;
step (205): deleting the low quality data in step (204);
step (206): obtaining a tropical cyclone wind speed change curve on the average of time and space;
step (207): deleting the low quality data in step (206);
a step (208): identifying a seventh-level wind ring and a tenth-level wind ring;
step (209): identifying a maximum wind speed radius;
and (3): it is determined whether all tropical cyclones and dates have been calculated.
2. The system of claim 1, wherein the system is characterized by the following features: the step (1) of determining the tropical cyclone and the date observed by the SFMR specifically comprises the following steps: and traversing the observation data folder, and determining the tropical cyclone and the observation date observed by all SFMRs according to the file names.
3. The system of claim 1, wherein the system is characterized by the following features: the reading of the optimal path data in step (201) specifically includes two situations:
the first method comprises the following steps: if the tropical cyclone has a record in the best path data, reading all months, dates, hours, central longitude and latitude of the tropical cyclone and the maximum wind speed of the tropical cyclone at the recorded moment;
and the second method comprises the following steps: if the tropical cyclone or the moment is not recorded in the best path data, the intensity is considered to be too low or the data is wrong, and subsequent calculation is not carried out.
4. The system of claim 1, wherein the system is characterized by the following features: the reading of the SFMR data in step (202) specifically includes: reading all SFMR aircraft observations on the day, wherein the read variables comprise longitude, latitude, flight altitude and SFMR wind speed; the aircraft observation in the step adopts 1 or more aircrafts for observation.
5. The system of claim 1, wherein the system is characterized by the following features: the step (203) of deleting the data of the take-off and landing process of the airplane comprises the following steps:
deleting SFMR data in the processes of taking off and landing;
recognizing a takeoff process: the initial time of SFMR observation is considered as the rising starting time; searching from the initial time, wherein the first time meets the condition that the flying height is more than 2000m, and more than 50% of flying points are in a non-rising stage in 20 minutes later, and the flying points are considered as rising ending time; the non-rising stage is the flying height of the next second or less;
and (3) identifying a landing process: the end time of the SFMR observation is regarded as the end time of the descent; searching forwards from the end time, wherein the first time meets the condition that the flying height is more than 2000m, and more than 50% of flying points in the first 20 minutes are in a non-descending stage and are considered as descending start time;
the non-descending stage is that the flying height is more than or equal to the flying height of the previous second;
and deleting the data of the takeoff process from the beginning to the end of ascending and deleting the data of the landing process from the beginning to the end of descending for the observation data of each airplane, thereby finishing the first data quality control.
6. The system of claim 1, wherein the system is characterized by the following features: converting the data in the step (203) into a three-dimensional array of time, distance and observation in the step (204) specifically comprises: interpolating the optimal path data to obtain the longitude and latitude of the tropical cyclone center at the observation time, and calculating to obtain the distance between the optimal path data and the tropical cyclone center according to the longitude and latitude of the airplane at the observation time; finally, all data are converted into a three-dimensional array of observation time, distance from the center of the tropical cyclone, and SFMR wind speed.
7. The system of claim 1, wherein the system is characterized by the following features: the deleting of the low quality data in the step (204) in the step (205) specifically includes: utilizing the optimal path data interpolation to obtain the maximum tropical cyclone wind speed at the observation moment; the difference of the maximum wind speed of the cyclone of the heat zone from the observed wind speed of the SFMR is required to be not more than 10m/s within 0-150km from the center of the cyclone of the heat zone, 150-300km is not more than 5m/s, more than 300km is not more than 0, otherwise, a great error is considered to exist, and data is deleted; and traversing all the data to complete the second data quality control.
8. The system of claim 1, wherein the system is characterized by the following features: the step (206) of obtaining the tropical cyclone wind speed variation curve by space-time averaging specifically includes: the arrays were averaged every 1km in space and every 6 hours in time to finally obtain the tropical cyclone wind speed level variation curves calculated from the SFMR observations.
9. The system of claim 1, wherein the system is characterized by the following features: the deleting of the low quality data in the step (206) in the step (207) specifically includes: deleting missing data: if no observation exists in the array of 0-400km after the space-time average, deleting the array of data;
deleting error data: for the array after the space-time average, deleting data of which the wind speed change exceeds 10m/s per kilometer from two adjacent points; if the maximum value of the array wind speed after the time-space averaging appears beyond 200km, deleting the array data;
and traversing all the space-time average results to finish the third time of data quality control.
10. The system of claim 1, wherein the system is characterized by the following features: the step (208) of identifying the seven-level and ten-level wind rings specifically comprises the following steps: identifying a seven-grade wind ring and a ten-grade wind ring by using the wind speed after space-time averaging;
the wind speed of the seventh-level wind ring is more than or equal to 13.9m/s, and the wind speed of the tenth-level wind ring is more than or equal to 24.5 m/s;
setting the search range of a seven-level wind circle to be 80km-500km, setting the search range of a ten-level wind circle to be 20km-300km, and requiring that non-empty points of 100km at the outermost side of the search range must contain 50% of points smaller than seven/ten-level wind, otherwise, considering that data is wrong and outputting default; the specific search scheme is as follows: searching inwards from the outermost side of the search range, identifying the position of a first point as a seven/ten-level wind circle if the inner side of the first point is 20km, more than 50% of the points are not empty, the average wind speed is more than seven/ten-level wind, and more than 80% of non-default points exceed a seven/ten-level wind lowest threshold; if there is no point that satisfies the condition, a default is output.
11. The system of claim 1, wherein the system is characterized by the following features: in the step (209), the radius of the maximum wind speed in the horizontal direction of the tropical cyclone is identified by using the array after space-time averaging;
setting a search range of 1-150km, and finding the maximum value as the maximum wind speed in the area with the maximum average value of 5 km. The maximum wind speed required to be found meets the following requirements: when the non-empty time is at 100-150km10 points or above, the maximum wind speed exceeds the average value of 100-150km for 5 m/s; when the non-space time is below 100-150km10 points, the maximum wind speed is greater than the average value of 0-100 km; if the condition is met, outputting the radius corresponding to the maximum wind speed as the maximum wind speed radius, and if the condition is not met, outputting a default.
12. The system of claim 1, wherein the system is characterized by the following features: the step (3) of determining whether all tropical cyclones and dates have been calculated specifically includes: if the calculation is not finished, calculating the next date or the next tropical cyclone, and if the calculation is finished, ending the program; if the calculation is not completed, the next date or the next tropical cyclone is calculated, and if the calculation is completed, the routine is ended.
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