CN111443399B - Tropical cyclone strong wind circle identification system based on stepping frequency microwave radiometer data - Google Patents
Tropical cyclone strong wind circle identification system based on stepping frequency microwave radiometer data Download PDFInfo
- Publication number
- CN111443399B CN111443399B CN201910966430.1A CN201910966430A CN111443399B CN 111443399 B CN111443399 B CN 111443399B CN 201910966430 A CN201910966430 A CN 201910966430A CN 111443399 B CN111443399 B CN 111443399B
- Authority
- CN
- China
- Prior art keywords
- data
- sfmr
- observation
- wind speed
- tropical cyclone
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01W—METEOROLOGY
- G01W1/00—Meteorology
- G01W1/10—Devices for predicting weather conditions
Landscapes
- Environmental & Geological Engineering (AREA)
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Atmospheric Sciences (AREA)
- Biodiversity & Conservation Biology (AREA)
- Ecology (AREA)
- Environmental Sciences (AREA)
- Indicating Or Recording The Presence, Absence, Or Direction Of Movement (AREA)
- Traffic Control Systems (AREA)
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.
In view of the current situation, international, especially in the united states which are also frequently attacked by tropical cyclone, mainly depends on airborne observation to determine seven-level and ten-level wind circles 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 cyclone (especially, open sea cyclone far from the coast) and tropical cyclone center, so as to better perform disaster prevention early warning, which has become an important technology for international tropical cyclone forecast early warning. Landsea and Franklin (2013) studies showed a significant decrease in uncertainty when windband decisions were made with airborne observations.
The airborne observation mainly comprises GPS (global positioning system) downward projection sounding, 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 (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.
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 (8)
1. A tropical cyclone strong wind circle identification system based on SFMR data of a step frequency microwave radiometer is characterized in that: the tropical cyclone strong wind circle identification system based on the SFMR data of the step frequency microwave radiometer 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;
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, converting all data into a three-dimensional array of observation time, distance from the tropical cyclone center and SFMR wind speed;
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): judging whether all tropical cyclones and dates are calculated;
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; traversing all data to complete the second data quality control;
the step (206) of obtaining the tropical cyclone wind speed variation curve by space-time averaging specifically includes: averaging the arrays every 1km in space and every 6 hours in time to finally obtain a tropical cyclone wind speed level change curve calculated according to SFMR observation;
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.
2. The system for identifying tropical cyclone strong wind circles based on SFMR data of step-frequency microwave radiometers of claim 1, wherein: 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 for identifying tropical cyclone strong wind circles based on SFMR data of step-frequency microwave radiometers of claim 1, wherein: 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 for identifying tropical cyclone strong wind circles based on SFMR data of step-frequency microwave radiometers of claim 1, wherein: 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 for identifying tropical cyclone strong wind circles based on SFMR data of step-frequency microwave radiometers of claim 1, wherein: 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 for identifying tropical cyclone strong wind circles based on SFMR data of step-frequency microwave radiometers of claim 1, wherein: 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.
7. The system for identifying tropical cyclone strong wind circle based on SFMR data of step-frequency microwave radiometer according to claim 1,
the method is characterized in that: 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 in an area with the maximum average value of 5km as the maximum wind speed; the maximum wind speed required to be found meets the following requirements: when 10 or more observation points in the 100-150km strip area are non-empty, the data exists in the non-empty state, and the maximum observed wind speed exceeds the average wind speed of 5m/s in the 100-150km strip area; when less than 10 observation points are not empty in the 100-150km strip area, the maximum wind speed is greater than the average wind speed in the 0-100km strip area; 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.
8. The system for identifying tropical cyclone strong wind circles based on SFMR data of step-frequency microwave radiometers of claim 1, wherein: the step (3) of determining whether all tropical cyclones and dates have been calculated specifically includes: 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.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910966430.1A CN111443399B (en) | 2019-10-12 | 2019-10-12 | Tropical cyclone strong wind circle identification system based on stepping frequency microwave radiometer data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910966430.1A CN111443399B (en) | 2019-10-12 | 2019-10-12 | Tropical cyclone strong wind circle identification system based on stepping frequency microwave radiometer data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111443399A CN111443399A (en) | 2020-07-24 |
| CN111443399B true CN111443399B (en) | 2021-09-28 |
Family
ID=71626820
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910966430.1A Active CN111443399B (en) | 2019-10-12 | 2019-10-12 | Tropical cyclone strong wind circle identification system based on stepping frequency microwave radiometer data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111443399B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114942481B (en) * | 2022-06-08 | 2023-07-18 | 中国气象局上海台风研究所(上海市气象科学研究所) | Tropical cyclone process extreme value wind speed probability forecasting method and device and computer equipment |
| CN114910980B (en) * | 2022-06-08 | 2023-07-18 | 中国气象局上海台风研究所(上海市气象科学研究所) | Wind ring forecasting method based on subjective path intensity forecasting and parameterized wind field model |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101933448A (en) * | 2010-07-26 | 2011-01-05 | 北京师范大学 | Method for manufacturing tropical cyclone wind zone |
| CN102132662A (en) * | 2011-01-10 | 2011-07-27 | 北京师范大学 | Improved method for making tropical cyclone wind zone |
| CN105388536A (en) * | 2015-11-10 | 2016-03-09 | 中国科学院深圳先进技术研究院 | Forecasting method and system of speed of instantaneous extremely strong wind caused by tropical cyclone at coastal region |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7970543B2 (en) * | 2008-03-18 | 2011-06-28 | The United States Of America, Represented By The Secretary Of Commerce | Predicting tropical cyclone destructive potential by integrated kinetic energy according to the Powell/Reinhold scale |
-
2019
- 2019-10-12 CN CN201910966430.1A patent/CN111443399B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101933448A (en) * | 2010-07-26 | 2011-01-05 | 北京师范大学 | Method for manufacturing tropical cyclone wind zone |
| CN102132662A (en) * | 2011-01-10 | 2011-07-27 | 北京师范大学 | Improved method for making tropical cyclone wind zone |
| CN105388536A (en) * | 2015-11-10 | 2016-03-09 | 中国科学院深圳先进技术研究院 | Forecasting method and system of speed of instantaneous extremely strong wind caused by tropical cyclone at coastal region |
Non-Patent Citations (4)
| Title |
|---|
| A new aircraft hurricane wind climatology and applications in assessing the predictive skill of tropical cyclone intensity using high-resolution ensemble forecasts;Judt Falko 等;《GEOPHYSICAL RESEARCH LETTERS》;20150728;第42卷(第14期);6043-6050 * |
| Hurricane Surface Wind Measurements from an Operational Stepped Frequency Microwave Radiometer;UHLHORN ERIC W.等;《MONTHLY WEATHER REVIEW》;20070930;第135卷(第9期);3070-3085 * |
| Improved Stepped Frequency Microwave Radiometer Tropical Cyclone Surface Winds in Heavy Precipitation;Klotz, Bradley W.等;《JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY》;20141130;第31卷(第11期);2392-2408 * |
| Stepped Frequency Microwave Radiometer Wind-Speed Retrieval Improvements;Sapp JosephW.等;《remote sensing》;20190122;第11卷(第3期);1-25 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111443399A (en) | 2020-07-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Dodge et al. | The kinematic structure of a hurricane with sea level pressure less than 900 mb | |
| Lombardo et al. | Automated extraction and classification of thunderstorm and non-thunderstorm wind data for extreme-value analysis | |
| Allen et al. | A severe thunderstorm climatology for Australia and associated thunderstorm environments | |
| CN111443399B (en) | Tropical cyclone strong wind circle identification system based on stepping frequency microwave radiometer data | |
| Bravin et al. | An investigation into location and convective lifecycle trends in an ice crystal icing engine event database | |
| Mohapatra et al. | Construction and quality of best tracks parameters for study of climate change impact on tropical cyclones over the North Indian Ocean during satellite era | |
| Riehl et al. | The recurvature of tropical storms | |
| Ratnam et al. | Assessment of GPS radiosonde descent data | |
| Böttger et al. | Forecasting extratropical storms with hurricane intensity using satellite information | |
| CN111399084B (en) | High-altitude rapid flow extraction method based on three-dimensional wind field data | |
| CN114325874B (en) | Method for establishing strong convection weather personal library system | |
| Landolt et al. | The impacts of automation on present weather–type observing capabilities across the conterminous United States | |
| Wakimoto | The West Bend, Wisconsin storm of 4 April 1981: A problem in operational meteorology | |
| Sharma et al. | Standard operation procedure for tropical cyclone vital parameters over North Indian Ocean | |
| Watson et al. | The relationship of lightning to surface convergence at Kennedy Space Center: A preliminary study | |
| CN110852169B (en) | Method for identifying typhoon maximum wind speed radius based on airborne data | |
| Zhou et al. | The wind and temperature information of AMDAR data applying to the analysis of severe weather nowcasting of airport | |
| CN110908013A (en) | Tropical cyclone prevention method | |
| Brown et al. | Cloud‐to‐ground lightning associated with the evolution of a multicell storm | |
| CN116449461B (en) | Automatic identifying method for south branch groove | |
| Kim et al. | Vertical structure and microphysical characteristics of Typhoon Kompasu (2010) at landfall | |
| Rae | A modified supercell composite parameter for supercell thunderstorms over the Gauteng Province, South Africa | |
| Chen et al. | Shipping routes in the South China Sea and northern Indian Ocean and associated monsoonal influences | |
| Reber | The south platte valley tornadoes of june 7, 1953 | |
| Fujiwara et al. | Characteristics of hailfall and lightning in a splitting thunderstorm observed on May 4, 2019 in the Tokyo Metropolitan Area, Japan |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |