CN108693534A - NRIET X band radars cooperate with networking analysis method - Google Patents
NRIET X band radars cooperate with networking analysis method Download PDFInfo
- Publication number
- CN108693534A CN108693534A CN201810260040.8A CN201810260040A CN108693534A CN 108693534 A CN108693534 A CN 108693534A CN 201810260040 A CN201810260040 A CN 201810260040A CN 108693534 A CN108693534 A CN 108693534A
- Authority
- CN
- China
- Prior art keywords
- radar
- precipitation
- wind
- data
- dimensional
- 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.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/95—Radar or analogous systems specially adapted for specific applications for meteorological use
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Abstract
The invention discloses a kind of NRIET X-band radars to cooperate with networking analysis method, including:Data collection.Data quality control.Using distance weighting interpolation technique, using three-dimensional cressman interpolation methods, to radar return, diameter is aweather and each amount of polarization carries out lattice point interpolation.Three-dimensional data networking.For the dimensional wind of Dual-Doppler weather radar inverting, WIND FIELDS is constrained using quality continuity equation, equation acquisition dimensional wind is solved by iterating;Vertical speed w is acquired from setting coboundary to the quality continuity equation of lower integral point, and using micro boundary condition adjustment vertical movement field.Composite reflectivity picture mosaic product, echo high picture mosaic product are generated based on Radar Products reflectivity algorithm;By Z-R relationships, quantitative precipitation estimation is carried out, obtains raininess product;Using texture classifying method, precipitation stratus convective region is identified;Using fuzzy logic algorithm, to different phase in Precipitation Process, the identification of each region rainfall particle phase.
Description
Technical field
The present invention relates to a kind of radar interpolation networking, Wind-field Retrieval and weather recognition methods more particularly to a kind of NRIET
X-band radar cooperates with networking analysis method.
Background technology
Monitoring, forecast and research mesoscale weather system are always the vital task of weather forecast, also always air section
A great problem, main difficulty are us to the three-dimensional that space scale is hundreds of kilometer or even tens kilometers of strong convection system
Structure, the mechanism of occurrence and development, in addition microphysical processes in strong convection know about it is very few.In the various means of atmospheric sounding
In, the remote sensing ability of weather radar with its high-spatial and temporal resolution, promptly and accurately becomes diastrous weather, especially mesoscale
Diastrous weather monitors and short-time weather forecasting etc. extremely effective tool.The utilization of weather radar is greatly deepened
To downburst, the understanding and cognition of the weather phenomena such as spout supercell, hail, squall line and mesoscale convective system improves
To the pre-alerting ability of diastrous weather.
China starts not giving up China New Generation Weather Radar net since nineteen ninety-eight, to burst heavy rain, coastal typhoon and precipitation
The Short-term Forecast accuracy rate of equal diastrous weathers is more many than improving originally, and timeliness is advanced by dozens of minutes to a few hours,.But
It is that New Generation Radar has the limitation of its own:Since (radar can only be in 0.5~19.5 ° of elevation coverage with one for scanning strategy
Fixed interval is scanned) and earth curvature etc. influence, cross very much region even if having if in effective radius of investigation of radar
It cannot be arrived by radar observation, for example, between the cone of silence, minimum scanning elevation angle data clear area below, adjacent elevation angle interval
The region that data clear area, landform stop radar beam etc..China 1km is with radar coverage only up to 17% at this stage, this
A little defects hinder us to the complete careful tracking observation of diastrous weather system, to reduce our these weather systems of team
The monitoring of system and prediction ability;Also counteract that we further disclose the various rule with the relevant air motion of these weather systems
Interaction between rule and various scales so that we can not make the occurrence and development mechanism of these systems comprehensive, objective
It sees, careful research.
Existing networking interpolation algorithm mainly has:Vertical interpolation after bilinear interpolation, Barnes interpolation methods and the conical surface
Method.But bilinear interpolation is affected by scarce survey, for lacking the case where surveying on a large scale, interpolation can have any problem.And
During radar observation, the place away from radar farther out, two observation points are apart from larger, but radial velocity is a vector, interval
It is excessive to be not suitable for interpolation.Barnes interpolation methods are mainly average weighted algorithm, calculate simply, can filter out partial noise, but
Be interpolation result and surrounding observation point uniformity coefficient and radius of influence relationship it is larger;The method of vertical interpolation after the first conical surface,
During Interpolation Process is macro, if data arduousness crosses the conference not discontinuous phenomenon of this case when vertical interpolation, it is larger to generate error.
China New Generation Weather Radar is S-band weather radar, for the Small and Medium Sizeds strong convection such as spout, thunderstorm, precipitation
Observation, spatial and temporal resolution precision are inadequate.However, Small and Medium Sized strong convection, including short-time strong rainfall, thunder and lightning, hail, thunderstorm gale
Deng being one of China's major weather disaster type.Strong convective weather temporarily, to be often accompanied with lightning flashes and thunder rumbles, wind heavy rain is anxious etc.
Bad weather, this weather disruptions power is very strong, be after being only second to tropical cyclone, earthquake, flood the 4th have it is anti-personnel
Diastrous weather.Meanwhile it is shorter when Small and Medium Sized strong convection life, variation is very fast.Currently, in meteorological observation business, monitoring
Small and Medium Sized strong convection relies primarily on New Generation Doppler Weather Radar, combined ground observation, wind profile radar, meteorological satellite etc.
Data.However, being limited by observation, spatial and temporal resolution is relatively low, more single etc. reasons of observation element type, still cannot continuously monitor,
Track generation, the development of Small and Medium Sized strong convection, internal structure, evolution to Small and Medium Sized strong convection diastrous weather
Solve unclear, the forecasting and warning that these all constrain Small and Medium Sized strong convection is horizontal.
The existing business radar network composite in China is the PPI products with the elevations angle CAPPI or low of a height of reflectivity
Or the two-dimentional picture mosaic that the Radar Products such as composite reflectivity carry out, and these domestic picture mosaics are to the quality of monostatic radar data
Analysis and control are seldom, these two-dimentional mosaic products can only be not suitable for analyzing application again to synoptic process qualitative analysis.It is right
The subjective analysis of forecaster is mainly still needed in the judgement of Convective Weather System, greatly reduces forecast efficiency;Thunder simultaneously
The diameter for taking things philosophically survey aweather only shows that weather system does not have dimensional wind product with respect to the moving direction and wind speed of radar, lacks to strong
The understanding of the dynamic structure of Precipitation Process.
China's China New Generation Weather Radar is single polarimetric radar at this stage, major product be radar reflectivity, radial velocity with
And spectrum width, the Convergence and divergence of precipitation power, wind speed and wind field can be mainly obtained by these products, it can not be to the micro- of precipitation
Physical arrangement, rainfall particle phase are analyzed, and the understanding to precipitation microphysical processes is affected.
Pattern is relatively low to the prediction ability of precipitation, and TS scorings are largely dependent upon micro- less than 0.2. precipitation forecasts
Physical parameter scheme, and forecast of the current Microphysical scheme to our region precipitations, especially fine structure
Prediction ability it is all insufficient.Previous studies are to study the feature of convection current based on radar reflectivity factor mostly, and to speck
The research for managing feature is also fewer.
Invention content
Low for 1km or less radar observation coverage rates, minimum elevations clear area below and accuracy of observation are not high enough
Problem is observed using NERIT X-band radar networkings, mend blind to China New Generation Weather Radar observation blind area, is improved simultaneously
The spatial resolution of observation system cooperates with networking observation to improve temporal resolution by radar.
The problem of can only carrying out qualitative analysis to synoptic process for two-dimentional mosaic product, use three-dimensional cressman
Interpolation obtains the three dimensional field information of synoptic process, and carry out to multi-section radar by the radar body total number according to being interpolated into three-dimensional lattice point
Networking obtains large-scale three-dimensional data, to show in entire synoptic process, the precipitation information of each height layer.
Lack aerodynamic field information for mosaic at this stage, is used using two radars double more under cartesian coordinate system
General Le radar wind field inversion method carries out Wind-field Retrieval, obtains dimensional wind (u, v, w).
It for the problem that radar network composite product is single, can only qualitatively judge, use the amount of polarization combination thunder of dual polarization radar
Up to reflectivity, radial velocity, the severe weather process such as strong convection precipitation, squall line, spout, thunderstorm and hail are identified.For strong day
The forecasting and warning of gas process provides support.
For problem insufficient to telling about the research of Microphysical Characteristics by force at this stage, observed using dual polarization radar, in conjunction with
Power field structure reinforces the understanding to precipitation microphysical processes.
The present invention carries out mending spatial and temporal resolution that is blind, while improving observation by observing blind area to weather radar.It is different from
Existing networking interpolation algorithm, present invention introduces distance weighting interpolation techniques, using three-dimensional cressman interpolation methods, to radar
Echo, diameter is aweather and each amount of polarization carries out lattice point interpolation, networking;Using ODD methods, area's radial direction style is overlapped to two radars
Point data carries out dimensional wind inverting;Precipitation echo is divided into Convective using radar reflectivity factor using texture classifying method
With stratus precipitation;Precipitation intensity and precipitation are calculated using radar reflectivity factor strong team, carry out quantitative precipitation estimation, it is quantitative
Offer precipitation intensity information;Using fuzzy logic algorithm, in conjunction with dual polarization radar dual-polarization amount by precipitation grain in precipitation echo
The various phases (rain, snow, hail, graupel, ice crystal etc.) of son identify.Be conducive to the visual understanding to Precipitation Process, convenient for strong
The observation early warning of weather system.
In order to solve problem above, present invention employs following technical solutions:A kind of NRIET X-band radars collaboration networking
Analysis method, including the following contents:
1, data collection
The X frequency band dual polarization radar observation data and periphery China New Generation Weather Radar Data for collecting networking, pass through new one
Observe a wide range of system on a large scale for weather radar, the system that X-band radar is observed according to weather radar carries out networking observation,
Obtain the higher radiosonde observation data of spatial and temporal resolution.
2, data quality control
The characteristics of according to X-band weather radar, quality control module includes second trip echo removal, decaying is corrected, speed is moved back
The processing such as fuzzy, land clutter and superrefraction echo filtering, and the quality of radar data data is highly susceptible to terrain shading,
Therefore it needs to do quality control to Radar Data, keeps observational data more accurate.To radiosonde observation data respectively by being based on thunder
It carries out quality control treatments with the quality control measure based on signal spectrum up to the reflectance data quality control measure of base data and obtains
To accurate, complete and in due course weather radar data.
● band radar data quality control diameter aweather, reflectivity
The problems such as more for domestic radar noise, by it is smooth, filter, fill up scarce method of determining and calculating and eliminate isolated point, fill up
It lacks and surveys, radar data is made to keep continuous;Using adaptive reference section reflectivity quality control algorithm, obtained using adaptive strategy
TDBZ the and VTDZ threshold values obtained, change back speed degree fuzzy algorithmic approach, and then move back Fuzzy Processing to speed.
Step 3, three-dimensional grid interpolation
In the case where considering the influences such as earth curvature atmospheric refraction, the spherical coordinates of observational data is switched to horizontal and vertical
Vertical coordinate system;Using distance weighting interpolation technique, using three-dimensional cressman interpolation methods, to radar return, diameter is aweather
And each amount of polarization carries out lattice point interpolation;
Step 4, three-dimensional data networking
By in each portion's radar three-dimensional Grid data group to unified three dimensional network lattice point, to radar observation overlapping region, some
The object for appreciation Grid data that multi-section radar on mesh point detects calculates overlapping region reflectivity factor according to distance weighting method
Data of the numerical value as the mesh point.
Step 5, networking product identification, inverting
Composite reflectivity picture mosaic product, echo high picture mosaic product are generated based on Radar Products reflectivity algorithm;Pass through Z-R
Relationship carries out quantitative precipitation estimation, obtains raininess product;Using texture classifying method, precipitation stratus convective region is identified;It utilizes
Fuzzy logic algorithm, to different phase in Precipitation Process, each region rainfall particle phase identifies.
Step 6, group web area double doppler Wind-field Retrieval
For the dimensional wind of Dual-Doppler weather radar inverting, WIND FIELDS is constrained using quality continuity equation, by repeatedly
It iteratively solves equation and obtains dimensional wind;Wherein, vertical speed w is asked from setting coboundary to the quality continuity equation of lower integral
, and using micro boundary condition adjustment vertical movement field.
The step 3 specifically includes the following contents:Radar is stereopsis, and conical surface surface sweeping is done at the defined elevation angle, former
Beginning radiosonde observation data is stored with spherical coordinate system.During networking, considers the influences such as earth curvature atmospheric refraction, will observe
The spherical coordinates of data switchs to horizontal and vertical vertical coordinate system (xj,yj,zj).Set the radius of influence (R), then calculating observation point j
Weight coefficient (W (D away from three dimensional network lattice point (x, y, the z) distance setj)):
Dj=[(x-xj)2+(y-yj)2+(z-zj)2]1/2
To all observation point f in the radius of influence on each mesh pointjObservation be weighted it is average:
In the three dimensional network lattice point of radar network composite, more radar observation overlapping regions observation is corrected by decaying and waits quality controls
Later, it is weighted to obtain each variable number of each mesh point according to each distance of the radar observation value away from radar on mesh point
Value.To obtain radar reflectivity, diameter in group web area aweather and the dual-polarizations amount such as reflectance difference rate and related coefficient three
Tie up Grid data.Composite reflectivity picture mosaic product, echo high picture mosaic product are generated based on Radar Products reflectivity algorithm simultaneously.
The step 5 specifically includes the following contents:
● quantitative analysis
By radar weather equation it is found that the intensity of radar reflectivity factor is related with the precipitation particles spectrum in sampling volume,
And precipitation intensity equally is composed to obtain by precipitation particles, therefore the pass between radar reflectivity factor and precipitation intensity can be utilized
System, establishes empirical equation:Z-R relationships carry out pinch-reflex ion diode.The Microphysical Structure of different precipitation type is different, Z-R empirical equations
There are difference, the present invention to use different Z-R relationship (convective precipitations for different precipitation type:Z=230.85R1.34, stratus drop
Water:Z=193.73R1.54, convection current and the total precipitation Z=231.44R of stratus1.34) quantitative precipitation estimation is carried out, it obtains with accurately dropping
Water information.
● stratus convection current is classified
The present invention use texture classifying method, using radar observation to reflectivity factor precipitation echo is divided into Convective
With stratus precipitation classification.The 3km obtained by networking interpolation ascend a height the radar reflectivity Grid data on face carry out convection current drop
Moisture class:
Z≥40 (8)
What arbitrary lattice point met equation (8) and (9) is all defined as convective core, and wherein Zbg is the radius using lattice point as the center of circle
For the average value of all non-zero precipitation reflectivity factors in the border circular areas of 11km, Δ Z is lattice point reflectivity factor and is averaged anti-
Penetrate the difference of rate factor Z bg.It, will be centered on convective core, using r as the lattice point in the border circular areas of radius after determining convective core
It is all identified as convective precipitation, shown in the definition such as equation (10) of r:
● rainfall particle phase identifies
The algorithm core of the present invention is fuzzy logic algorithm, is broadly divided into three parts:1) it is blurred, 2) rule-based reasoning,
It is fuzzy with 3) degenerating, it is specific as follows:The radar reflectivity factor that is arrived using dual-polarization Doppler weather radar observation precipitation measurement and double
Amount of polarization:Reflectance difference rate, related coefficient poorer than differential phase, height and temperature etc., by fuzzy logic algorithm into professional etiquette
Then reasoning, then classified by the fuzzy precipitation phase that obtains of degenerating.
Blurring is that the accurate radar variable of input is converted to the process of the fuzzy set with corresponding degree of membership.Profit
The 10 class phase types with the radar variable of each input for identification establish 10 fuzzy sets, are indicated using member function, this
Text uses member's beta function:
Wherein, x indicates that observation value, m indicate that central point, a indicate that inflection point half width, b indicate the slope of curve.The present invention makes
The input radar variable used has:Radar reflectivity factor (Z), Analysis of Differential Reflectivity Factor Measured (ZDR), than difference to displacement (KDP)
And CC, while the temperature data on the obtained each height layer of sounding station is also a significant variable.Utilize fuzzy logic algorithm
Precipitation particles are divided into ten kinds of phases:Light rain, ice crystal, polymer, snow slush, is perpendicularly oriented to ice crystal, low-density graupel, high density at rain
Graupel, hail and big raindrop.The distribution of corresponding various amount of polarization when each phase weight is obtained by calculation close to 1, such as
Shown in Fig. 2.
The step 6 specifically includes the following contents:Lattice point radial direction wind data is utilized under cartesian coordinate system, by double
The use of Doppler wind retrieval method carries out dimensional wind inverting.The Wind-field Retrieval method is to utilize the radial speed of radar
The geometrical relationship of degree and dimensional wind (u, v, w) constrains WIND FIELDS using quality continuity equation, passes through the solution side that iterates
Journey obtains dimensional wind, and specific inversion method is as follows:
If (xi, yi, zi) indicate i-th of radar position, with u, v, w=w0+VtRepresentative is transported on (x, y, z) lattice site
Dynamic particle is in x, y, the component on the directions z, wherein VtIndicate particle falling speed, w0It indicates air vertical speed, then measures
Radar radial velocity Vi is expressed as under cartesian coordinate system:
Wherein:
Ri=(x-xi)2+(y-yi)2+(z-zi)2
Had to the radar radial velocity that first observes by (1):
Had to the radar radial velocity that second observes by (1):
Using two radar radial velocities, equation (2) and (3) show that horizontal wind speed u and v are:
Vertical speed w usually to lower integral or sets quality continuity equation of the lower boundary to upper integral from setting coboundary
It acquires, since the influence of Horizontal Winds error, and the up-and-down boundary condition of integral are swept with low layer to high level in integral process
The influence for retouching the reasons such as time difference, to cause vertical speed error.The minimum of remaining radar scanning is synthesized in the present invention to face upward
Between angle can reach 0.5 km to 2.5km, the highest elevation angle can to 15km or more, therefore in the present invention vertical velocity field use to
(coboundary takes 15km to the method for lower integral, no matter Echo Rating size, takes w=0ms-1).Specific vertical speed computational methods are as follows:
First assume w=0, initial level wind speed u, v of each mesh point are acquired by equation (4) and (5), recycles equation
(6) it is integrated and is not found out modified indication vertical speed w.The mesh point w found out is substituted into equation (4) and (5) again, it is anti-in this way
Multiple iteration, it is known that u, v and w of dimensional wind reach required precision.Wherein, vertical speed is to use the side up and down while integrated
Method.
During above Dual-Doppler weather radar Wind-field Retrieval, lack object since geometrical relationship solves the wind field come
The consistency of reason, therefore wind field is further adjusted with variational method, so that the wind field after adjustment is met the physical limit of continuity equation.
Since radar beam is too high with height point, analysis lattice point can not observe lower boundary apart from the place of radar farther out
Wind field, cause vertical speed to be underestimated.The problem of for lower boundary wind field undersampling, is adjusted vertical using micro boundary condition
Sports ground, method are as follows:
Wherein B is up-and-down boundary, and Δ Z is Vertical Patterns,For horizontal divergence, f indicates regulation coefficient, takes f=herein
1;Will the lower boundary of script extend downwardly 1 vertical grid spacing.
The present invention has following advantageous effect for the immediate prior art:
China New Generation Weather Radar observation blind area is carried out mending time-space resolution that is blind, while improving observation using the method for the present invention
Rate.Different from existing networking interpolation algorithm, present invention introduces distance weighting interpolation techniques, using the three-dimensional interpolation sides cressman
Method, to radar return, diameter is aweather and each amount of polarization carries out lattice point interpolation, networking;Using ODD methods, two radars are overlapped
Area's radial direction wind Grid data carries out dimensional wind inverting;Utilize radar reflectivity factor by precipitation echo using texture classifying method
It is divided into Convective and stratus precipitation;Precipitation intensity and precipitation are calculated using radar reflectivity factor strong team, carry out quantitative drop
Water estimation, quantitative offer precipitation intensity information;Using fuzzy logic algorithm, precipitation is returned in conjunction with dual polarization radar dual-polarization amount
The various phases of precipitation particles (rain, snow, hail, graupel, ice crystal etc.) identify in wave.Be conducive to recognize the intuitive of Precipitation Process
Know, convenient for the observation early warning to strong weather system.
Description of the drawings
Fig. 1 is the flow chart that NRIET X-band radars of the present invention cooperate with networking analysis method.
Fig. 2 be each phase weight close to 1 when corresponding various amount of polarization distribution figure.
Specific implementation mode
1 couple of present invention illustrates below in conjunction with the accompanying drawings.
As shown in Figure 1, for during Precipitation in Jiang-Huai Mei-yu, with the primary in short-term by Precipitation Process of sharp side, utilize
Radar network composite method, to precipitation power, microphysical processes are analyzed.
1, the radar data for needing networking is collected, the sharp side precipitation data observed using radar network carry out quality
By radar network composite after control, three dimensional field Precipitation Structure is obtained, networking data time resolution ratio is 6 minutes, and space level is differentiated
Rate is 1km, and vertical distribution rate is 0.5km.
2, by the precipitation echo horizontal distribution figure on 3km contour planes in three-dimensional grid point data, Texture classification side is utilized
Method identifies stratus convective region.It can be seen that during this time Precipitation Process is mainly embedded in large stretch of band-like frontal rain
The short-time strong rainfall composition that small scale strong convection generates.It can see the vertical junction of Precipitation Process by three-dimensional Grid data
Structure:The convective core development of most of convection current reaches 5km height, and part strong convection 35dBZ, which rises, reaches 7km height, convective echo
It rises more than 12km, convection intensity is strong, and development height is high.
3, networking observation area is obtained using Dual-Doppler weather radar Wind-field Retrieval, the Heavy Precipitation interior three-dimensional observed
Power field structure.Wind field in low layer has apparent convergence, air-flow convergence lifting, steam lifting to be condensed into raindrop;Strong convection precipitation position,
The air-flow rate of climb is larger, reaches maximum in middle level.The strong rate of climb brings raindrop into melt layer, the presence of melt layer subcooled water
So that echo strength is stronger, upward raindrop condense rapidly, reflectance difference rate decrease fast, discharge latent heat, promote convection current further
Development upwards, ice-phase is stronger, generates the ice phase particles such as graupel.Ice phase particle, which melts, to fall, and generates larger raindrop, at this time radar
Echo and reflectance difference rate value are larger.
4, radar quantitative precipitation estimation is shown, strong convection band radar echo is stronger, generates short-time strong rainfall.
5, it is found by precipitation particles precipitation classification, during summer bai-u rainy period convective precipitation, melt layer is mainly once
Raindrop, based on stratus area melt layer is predominantly avenged, and convective region is with the presence of the ice phase particle such as hail, graupel particle, and upper elevator
Dynamic stronger, ice phase particle kind is more, while hail and graupel particle development height are higher.And it is with ice crystal mainly in tropopause
It is main.Ascending motion is stronger, and the development of ice phase particle is stronger, and it is big raindrop that the landing of ice phase particle, which is melted, or generates hail weather.With
It is merely a preferred embodiment of the present invention described in upper, is not restricted to the present invention, for those skilled in the art, this
Invention can have various modifications and variations.Any modification, equivalent replacement made by all within the spirits and principles of the present invention,
Improve etc., it should be included within scope of the presently claimed invention.
Claims (4)
1. a kind of NRIET X-band radars cooperate with networking analysis method, which is characterized in that including the following contents:
Step 1, data collection
Collection group X frequency band dual polarization radar observes data and periphery China New Generation Weather Radar Data, passes through weather thunder of new generation
Up to a wide range of system is observed on a large scale, the system that X-band radar is observed according to weather radar carries out networking observation, obtains space-time
The higher radiosonde observation data of resolution ratio;
Step 2, data quality control
The characteristics of according to X-band weather radar, quality control module include second trip echo removal, decaying correct, speed move back it is fuzzy,
Land clutter and superrefraction echo filtration treatment;Reflectance data based on base data is passed through respectively to radiosonde observation data
Quality control measure and quality control measure based on signal spectrum carry out quality control treatments;
● band radar data quality control diameter aweather, reflectivity
For the problem more than domestic radar noise, by it is smooth, filter, fill up scarce method of determining and calculating and eliminate isolated point, fill up scarce survey,
Radar data is set to keep continuous;Using adaptive reference section reflectivity quality control algorithm, obtained using adaptive strategy
TDBZ and VTDZ threshold values change back speed degree fuzzy algorithmic approach, and then move back Fuzzy Processing to speed.
Step 3, three-dimensional grid interpolation
In the case where considering that earth curvature atmospheric refraction influences, the spherical coordinates of observational data is switched to horizontal and vertical vertical
Coordinate system;Using distance weighting interpolation technique, using three-dimensional cressman interpolation methods, to radar return, diameter is aweather and respectively
Amount of polarization carries out lattice point interpolation;
Step 4, three-dimensional data networking
By in each portion's radar three-dimensional Grid data group to unified three dimensional network lattice point, to radar observation overlapping region, some grid
The object for appreciation Grid data that multi-section radar on point detects calculates overlapping region reflectivity factor numerical value according to distance weighting method
Data as the mesh point;
Step 5, networking product identification, inverting
Composite reflectivity picture mosaic product, echo high picture mosaic product are generated based on Radar Products reflectivity algorithm;It is closed by Z-R
System carries out quantitative precipitation estimation, obtains raininess product;Using texture classifying method, precipitation stratus convective region is identified;Utilize mould
Fuzzy logic algorithm, to different phase in Precipitation Process, each region rainfall particle phase identifies;
Step 6, group web area double doppler Wind-field Retrieval
For the dimensional wind of Dual-Doppler weather radar inverting, WIND FIELDS is constrained using quality continuity equation, by iterating
It solves equation and obtains dimensional wind;Wherein, vertical speed w is acquired from setting coboundary to the quality continuity equation of lower integral, and
Vertical movement field is adjusted using micro boundary condition.
2. NRIET X-band radars according to claim 1 cooperate with networking analysis method, which is characterized in that
The step 3 specifically includes the following contents:Radar is stereopsis, and conical surface surface sweeping, original thunder are done at the defined elevation angle
It is stored up to observational data with spherical coordinate system;During networking, the influence of earth curvature atmospheric refraction is considered, by observational data
Spherical coordinates switch to horizontal and vertical vertical coordinate (xj,yj,zj;Radius of influence R, then calculating observation point j are set away from setting
Three dimensional network lattice point x, y, z distance weight coefficient W (Dj):
Dj=[(x-xj)2+(y-yj)2+(z-zj)2]1/2
To all observation point f in the radius of influence on each mesh pointjObservation be weighted it is average:
In the three dimensional network lattice point of radar network composite, after more radar observation overlapping regions observation corrects quality control by decaying,
It is weighted to obtain each variable value of each mesh point according to each distance of the radar observation value away from radar on mesh point;To
Obtain in group web area radar reflectivity, diameter aweather and the three-dimensional lattice point number of reflectance difference rate and the dual-polarization amount of related coefficient
According to;Composite reflectivity picture mosaic product, echo high picture mosaic product are generated based on Radar Products reflectivity algorithm simultaneously.
3. NRIET X-band radars according to claim 1 cooperate with networking analysis method, which is characterized in that
The step 5 specifically includes the following contents:
● quantitative analysis
By radar weather equation it is found that the intensity of radar reflectivity factor is related with the precipitation particles spectrum in sampling volume, and drop
Water intensity equally is composed to obtain by precipitation particles, therefore can utilize the relationship between radar reflectivity factor and precipitation intensity, is built
Vertical empirical equation:Z-R relationships carry out pinch-reflex ion diode;The Microphysical Structure of different precipitation type is different, and Z-R empirical equations also have difference
Not, quantitative precipitation estimation is carried out using different Z-R relationships for different precipitation type, obtains more accurate precipitation information;Its
In, convective precipitation:Z=230.85R1.34, stratus precipitation:Z=193.73R1.54, convection current and the total precipitation Z=of stratus
231.44R1.34;
● stratus convection current is classified
Using texture classifying method, using radar observation to reflectivity factor precipitation echo is divided into Convective and stratus drops
Moisture class;The 3km obtained by networking interpolation ascend a height the radar reflectivity Grid data on face carry out convective precipitation classification:
Z≥40 (8)
Z<40 and
What arbitrary lattice point met equation (8) and (9) is all defined as convective core, and wherein Zbg is using lattice point as the center of circle, and radius is
The average value of all non-zero precipitation reflectivity factors in the border circular areas of 11km, Δ Z are lattice point reflectivity factor and average reflection
The difference of rate factor Z bg;After determining convective core, will centered on convective core, using r as the lattice point in the border circular areas of radius all
It is identified as convective precipitation, shown in the definition such as equation (10) of r:
● rainfall particle phase identifies
Fuzzy logic algorithm is algorithm core, including three parts:1) be blurred, 2) rule-based reasoning and 3) degenerate it is fuzzy, specifically
It is as follows:The radar reflectivity factor and dual-polarization amount arrived using dual-polarization Doppler weather radar observation precipitation measurement:Reflectance difference rate,
, related coefficient poorer than differential phase, height and temperature carry out rule-based reasoning by fuzzy logic algorithm, then pass through degeneration mould
Paste obtains the classification of precipitation phase;
Blurring is that the accurate radar variable of input is converted to the process of the fuzzy set with corresponding degree of membership;Using every
The radar variable of a input establishes 10 fuzzy sets for 10 class phase types of identification, is indicated using member function, this place makes
It is member's beta function:
Wherein, x indicates that observation value, m indicate that central point, a indicate that inflection point half width, b indicate the slope of curve;Using to input
Radar variable has:Radar reflectivity factor Z, Analysis of Differential Reflectivity Factor Measured ZDR, than difference to displacement KDPAnd CC, while sounding station obtains
To each height layer on temperature data be also that precipitation particles are divided into ten kinds by a significant variable using fuzzy logic algorithm
Phase:Light rain, ice crystal, polymer, snow slush, is perpendicularly oriented to ice crystal, low-density graupel, high density graupel, hail and big raindrop at rain.
4. NRIET X-band radars according to claim 1 cooperate with networking analysis method, which is characterized in that
The step 6 specifically includes the following contents:Lattice point radial direction wind data is utilized under cartesian coordinate system, by double how general
The use of radar wind field inversion method is strangled, dimensional wind inverting is carried out;The Wind-field Retrieval method be using radar radial velocity and
The geometrical relationship of dimensional wind (u, v, w) constrains WIND FIELDS using quality continuity equation, and by iterating, solution equation obtains
Take dimensional wind, specific inversion method as follows:
If (xi, yi, zi) indicate i-th of radar position, with u, v, w=w0+VtWhat representative moved on (x, y, z) lattice site
Particle is in x, y, the component on the directions z, wherein VtIndicate particle falling speed, w0Indicate air vertical speed, the then radar measured
Radial velocity Vi is expressed as under cartesian coordinate system:
Wherein:
Ri=(x-xi)2+(y-yi)2+(z-zi)2
Had to the radar radial velocity that first observes by (1):
Had to the radar radial velocity that second observes by (1):
Using two radar radial velocities, equation (2) and (3) show that horizontal wind speed u and v are:
Vertical speed w is usually asked from setting coboundary to lower integral or setting lower boundary to the quality continuity equation of upper integral
, since the influence of Horizontal Winds error, and the up-and-down boundary condition of integral are scanned with low layer to high level in integral process
The influence of time difference, to cause vertical speed error;Synthesizing the minimum elevations of remaining radar scanning in the present invention can reach
Between 0.5km to 2.5km, the highest elevation angle can be to 15km or more, therefore vertical velocity field is used to lower integral in the present invention
" coboundary takes 15km to method, no matter Echo Rating size, takes w=0ms-1";Specific vertical speed computational methods are as follows:
First assume w=0, acquire initial level wind speed u, v of each mesh point by equation (4) and (5), recycle equation (6) into
Row integral does not find out modified indication vertical speed w;The mesh point w found out is substituted into equation (4) and (5) again, is changed repeatedly in this way
Generation, it is known that u, v and w of dimensional wind reach required precision;Wherein, vertical speed is to use the method up and down while integrated;
During above Dual-Doppler weather radar Wind-field Retrieval, lack physics since geometrical relationship solves the wind field come
Consistency, therefore wind field is further adjusted with variational method, so that the wind field after adjustment is met the physical limit of continuity equation;
Since radar beam is too high with height point, analysis lattice point is in the wind that can not observe lower boundary apart from the place of radar farther out
, cause vertical speed to be underestimated;The problem of for lower boundary wind field undersampling, is adjusted using micro boundary condition and is moved vertically
, method is as follows:
Wherein B is up-and-down boundary, and Δ Z is Vertical Patterns,For horizontal divergence, f indicates regulation coefficient, takes f=1 herein;I.e.
The lower boundary of script is extended downwardly into 1 vertical grid spacing.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810260040.8A CN108693534A (en) | 2018-03-27 | 2018-03-27 | NRIET X band radars cooperate with networking analysis method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810260040.8A CN108693534A (en) | 2018-03-27 | 2018-03-27 | NRIET X band radars cooperate with networking analysis method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108693534A true CN108693534A (en) | 2018-10-23 |
Family
ID=63844613
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810260040.8A Pending CN108693534A (en) | 2018-03-27 | 2018-03-27 | NRIET X band radars cooperate with networking analysis method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108693534A (en) |
Cited By (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109444893A (en) * | 2018-11-14 | 2019-03-08 | 中国气象科学研究院 | Phase identification product pattern splicing method and device based on dual polarization radar net |
| CN109633783A (en) * | 2018-11-14 | 2019-04-16 | 中国气象科学研究院 | Parameter pattern splicing method and device based on dual polarization radar net |
| CN110297246A (en) * | 2019-07-16 | 2019-10-01 | 上海市气象信息与技术支持中心 | A kind of cooperative self-adapted control method of networking X-band weather radar and system |
| CN110376562A (en) * | 2019-07-30 | 2019-10-25 | 长威信息科技发展股份有限公司 | A kind of verification method of dual polarization radar weather forecasting accuracy |
| CN110488297A (en) * | 2019-08-30 | 2019-11-22 | 成都信息工程大学 | A kind of method for early warning of complex topographic territory hailstorm |
| CN110488393A (en) * | 2019-09-24 | 2019-11-22 | 中国科学院大气物理研究所 | X-band dual-polarization radar measurement method and system |
| CN110531360A (en) * | 2019-08-28 | 2019-12-03 | 中船重工鹏力（南京）大气海洋信息系统有限公司 | A kind of X-band weather radar networking data processing method |
| CN110703255A (en) * | 2019-09-19 | 2020-01-17 | 天津大学 | Doppler radial velocity diagram simulation method of typical strong convection flow field |
| CN110727719A (en) * | 2019-10-16 | 2020-01-24 | 中国气象局广州热带海洋气象研究所（广东省气象科学研究所） | Lightning positioning data assimilation method based on dynamic relaxation approximation |
| CN110780299A (en) * | 2019-10-24 | 2020-02-11 | 上海眼控科技股份有限公司 | Divergence field acquisition method and device, computer equipment and storage medium |
| CN111398964A (en) * | 2020-04-10 | 2020-07-10 | 中国水利水电科学研究院 | Radar nowcasting method based on strong precipitation recognition and numerical atmospheric mode driving |
| CN111398949A (en) * | 2020-04-10 | 2020-07-10 | 江苏省气象科学研究所 | Networking X-band radar-based self-adaptive collaborative scanning method |
| CN113655455A (en) * | 2021-10-15 | 2021-11-16 | 成都信息工程大学 | Dual-polarization weather radar echo signal simulation method |
| CN113777573A (en) * | 2021-08-30 | 2021-12-10 | 中船重工鹏力（南京）大气海洋信息系统有限公司 | Dual-polarization radar secondary echo identification method based on naive Bayes classifier |
| CN114488160A (en) * | 2022-04-02 | 2022-05-13 | 南京师范大学 | Radar rainfall estimation error correction method considering influence of three-dimensional wind field |
| CN115097459A (en) * | 2022-04-13 | 2022-09-23 | 泰州市气象局 | S and X waveband networking weather radar reflectivity factor interactive verification method and system |
| CN115575914A (en) * | 2022-12-07 | 2023-01-06 | 成都远望科技有限责任公司 | Multi-band dual-polarization weather radar observed quantity error quantification method |
| CN115932766A (en) * | 2022-12-15 | 2023-04-07 | 佛山市龙卷风研究中心 | Phased array radar data correction system |
| CN116027333A (en) * | 2023-02-24 | 2023-04-28 | 南京信息工程大学 | Method for generating three-dimensional scanning elevation angle parameters of microwave rain-measuring radar |
| CN116108338A (en) * | 2023-04-13 | 2023-05-12 | 北京弘象科技有限公司 | Dynamic set identification method and device for particle phase state |
| CN116184342A (en) * | 2023-04-27 | 2023-05-30 | 无锡智鸿达电子科技有限公司 | Cloud testing radar data calibration method and system based on multi-radar networking |
| CN116430390A (en) * | 2023-06-13 | 2023-07-14 | 南京信息工程大学 | S-band dual-polarization radar quality control method for data assimilation |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101988963A (en) * | 2010-04-19 | 2011-03-23 | 南京恩瑞特实业有限公司 | Method for acquiring three-dimensional wind field by using wind profiler radar |
| WO2013002768A1 (en) * | 2011-06-28 | 2013-01-03 | Vaisala Oyj | Off-diagonal element echo power estimator for polarization weather radar |
| KR20150066315A (en) * | 2013-12-06 | 2015-06-16 | 대한민국(기상청장) | Quantitative precipitation estimation system based dual polarization radars and method thereof |
| CN107064937A (en) * | 2017-06-07 | 2017-08-18 | 孝感市青谷信息科技有限公司 | A kind of measuring method of Dual-linear polarization radar system and strong rain |
| CN107831481A (en) * | 2017-10-30 | 2018-03-23 | 厦门市气象灾害防御技术中心(海峡气象开放实验室厦门市避雷监测技术中心) | The lower three Doppler radar three-dimensional wind field inversion methods of dynamic earth coordinate system |
-
2018
- 2018-03-27 CN CN201810260040.8A patent/CN108693534A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101988963A (en) * | 2010-04-19 | 2011-03-23 | 南京恩瑞特实业有限公司 | Method for acquiring three-dimensional wind field by using wind profiler radar |
| WO2013002768A1 (en) * | 2011-06-28 | 2013-01-03 | Vaisala Oyj | Off-diagonal element echo power estimator for polarization weather radar |
| KR20150066315A (en) * | 2013-12-06 | 2015-06-16 | 대한민국(기상청장) | Quantitative precipitation estimation system based dual polarization radars and method thereof |
| CN107064937A (en) * | 2017-06-07 | 2017-08-18 | 孝感市青谷信息科技有限公司 | A kind of measuring method of Dual-linear polarization radar system and strong rain |
| CN107831481A (en) * | 2017-10-30 | 2018-03-23 | 厦门市气象灾害防御技术中心(海峡气象开放实验室厦门市避雷监测技术中心) | The lower three Doppler radar three-dimensional wind field inversion methods of dynamic earth coordinate system |
Non-Patent Citations (3)
| Title |
|---|
| 吴彬: "双基地双线偏振多普勒雷达资料处理与应用", 《中国优秀博硕士学位论文全文数据库(硕士)基础科学辑》 * |
| 徐立理: "梅雨锋面对流的动力和微物理特征雷达观测研究", 《中国优秀博硕士学位论文全文数据库(硕士)基础科学辑》 * |
| 肖艳姣: "新一代天气雷达三维组网技术及其应用研究", 《中国优秀博硕士学位论文全文数据库(博士)基础科学辑》 * |
Cited By (32)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109444893A (en) * | 2018-11-14 | 2019-03-08 | 中国气象科学研究院 | Phase identification product pattern splicing method and device based on dual polarization radar net |
| CN109633783A (en) * | 2018-11-14 | 2019-04-16 | 中国气象科学研究院 | Parameter pattern splicing method and device based on dual polarization radar net |
| CN110297246A (en) * | 2019-07-16 | 2019-10-01 | 上海市气象信息与技术支持中心 | A kind of cooperative self-adapted control method of networking X-band weather radar and system |
| CN110297246B (en) * | 2019-07-16 | 2021-04-02 | 上海市气象信息与技术支持中心 | Networking X-waveband weather radar collaborative self-adaptive control method and system |
| CN110376562A (en) * | 2019-07-30 | 2019-10-25 | 长威信息科技发展股份有限公司 | A kind of verification method of dual polarization radar weather forecasting accuracy |
| CN110376562B (en) * | 2019-07-30 | 2022-10-11 | 长威信息科技发展股份有限公司 | Method for verifying weather prediction accuracy of dual-polarization radar |
| CN110531360A (en) * | 2019-08-28 | 2019-12-03 | 中船重工鹏力（南京）大气海洋信息系统有限公司 | A kind of X-band weather radar networking data processing method |
| CN110531360B (en) * | 2019-08-28 | 2021-08-17 | 中船重工鹏力（南京）大气海洋信息系统有限公司 | X-band weather radar networking data processing method |
| CN110488297A (en) * | 2019-08-30 | 2019-11-22 | 成都信息工程大学 | A kind of method for early warning of complex topographic territory hailstorm |
| CN110488297B (en) * | 2019-08-30 | 2023-03-24 | 成都信息工程大学 | Early warning method for hailstorms in complex terrain area |
| CN110703255A (en) * | 2019-09-19 | 2020-01-17 | 天津大学 | Doppler radial velocity diagram simulation method of typical strong convection flow field |
| CN110488393A (en) * | 2019-09-24 | 2019-11-22 | 中国科学院大气物理研究所 | X-band dual-polarization radar measurement method and system |
| CN110488393B (en) * | 2019-09-24 | 2020-05-15 | 中国科学院大气物理研究所 | X-band dual-polarization weather radar quantitative measurement precipitation method and system |
| CN110727719A (en) * | 2019-10-16 | 2020-01-24 | 中国气象局广州热带海洋气象研究所（广东省气象科学研究所） | Lightning positioning data assimilation method based on dynamic relaxation approximation |
| CN110780299A (en) * | 2019-10-24 | 2020-02-11 | 上海眼控科技股份有限公司 | Divergence field acquisition method and device, computer equipment and storage medium |
| CN111398949A (en) * | 2020-04-10 | 2020-07-10 | 江苏省气象科学研究所 | Networking X-band radar-based self-adaptive collaborative scanning method |
| CN111398964A (en) * | 2020-04-10 | 2020-07-10 | 中国水利水电科学研究院 | Radar nowcasting method based on strong precipitation recognition and numerical atmospheric mode driving |
| CN113777573A (en) * | 2021-08-30 | 2021-12-10 | 中船重工鹏力（南京）大气海洋信息系统有限公司 | Dual-polarization radar secondary echo identification method based on naive Bayes classifier |
| CN113777573B (en) * | 2021-08-30 | 2023-12-01 | 中船鹏力(南京)大气海洋信息系统有限公司 | Double-polarization radar secondary echo identification method based on naive Bayes classifier |
| CN113655455B (en) * | 2021-10-15 | 2022-04-08 | 成都信息工程大学 | Dual-polarization weather radar echo signal simulation method |
| CN113655455A (en) * | 2021-10-15 | 2021-11-16 | 成都信息工程大学 | Dual-polarization weather radar echo signal simulation method |
| CN114488160A (en) * | 2022-04-02 | 2022-05-13 | 南京师范大学 | Radar rainfall estimation error correction method considering influence of three-dimensional wind field |
| CN115097459A (en) * | 2022-04-13 | 2022-09-23 | 泰州市气象局 | S and X waveband networking weather radar reflectivity factor interactive verification method and system |
| CN115575914A (en) * | 2022-12-07 | 2023-01-06 | 成都远望科技有限责任公司 | Multi-band dual-polarization weather radar observed quantity error quantification method |
| CN115575914B (en) * | 2022-12-07 | 2023-03-10 | 成都远望科技有限责任公司 | Multi-band dual-polarization weather radar observed quantity error quantification method |
| CN115932766A (en) * | 2022-12-15 | 2023-04-07 | 佛山市龙卷风研究中心 | Phased array radar data correction system |
| CN115932766B (en) * | 2022-12-15 | 2024-03-22 | 佛山市龙卷风研究中心 | Phased array radar data correction system |
| CN116027333A (en) * | 2023-02-24 | 2023-04-28 | 南京信息工程大学 | Method for generating three-dimensional scanning elevation angle parameters of microwave rain-measuring radar |
| CN116108338A (en) * | 2023-04-13 | 2023-05-12 | 北京弘象科技有限公司 | Dynamic set identification method and device for particle phase state |
| CN116184342A (en) * | 2023-04-27 | 2023-05-30 | 无锡智鸿达电子科技有限公司 | Cloud testing radar data calibration method and system based on multi-radar networking |
| CN116430390A (en) * | 2023-06-13 | 2023-07-14 | 南京信息工程大学 | S-band dual-polarization radar quality control method for data assimilation |
| CN116430390B (en) * | 2023-06-13 | 2023-08-22 | 南京信息工程大学 | S-band dual-polarization radar quality control method for data assimilation |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108693534A (en) | NRIET X band radars cooperate with networking analysis method | |
| KR101531224B1 (en) | Quantitative precipitation estimation system based dual polarization radars and method thereof | |
| Liang et al. | A composite approach of radar echo extrapolation based on TREC vectors in combination with model-predicted winds | |
| Goudenhoofdt et al. | Generation and verification of rainfall estimates from 10-yr volumetric weather radar measurements | |
| CN110082842B (en) | Precipitation estimation method and device | |
| Zhang et al. | Radar quality index (RQI)—A combined measure of beam blockage and VPR effects in a national network | |
| CN104569981B (en) | Synergy self-adaption observing method | |
| Panziera et al. | The relation between airflow and orographic precipitation on the southern side of the Alps as revealed by weather radar | |
| Kirstetter et al. | A physically based identification of vertical profiles of reflectivity from volume scan radar data | |
| Hashiguchi et al. | Precipitation microstructure in different Madden–Julian Oscillation phases over Sumatra | |
| Kumar et al. | On the effects of large-scale environment and surface types on convective cloud characteristics over Darwin, Australia | |
| Jordan et al. | Sampling errors in radar estimates of rainfall | |
| CN107356926A (en) | Difference cloud cluster extrapolation precipitation predicting algorithm based on Hu squares | |
| Chang et al. | An application of reflectivity climatology in constructing radar hybrid scans over complex terrain | |
| CN111289983B (en) | Inversion method for vertically accumulated liquid water content of radar | |
| Welsh et al. | Understanding heavy lake-effect snowfall: The vertical structure of radar reflectivity in a deep snowband over and downwind of Lake Ontario | |
| Trombe et al. | Weather radars–the new eyes for offshore wind farms? | |
| Beck | Generation of spatially correlated synthetic rainfall time series in high temporal resolution: a data driven approach | |
| Lee et al. | Enhancement of orographic precipitation in Jeju Island during the passage of Typhoon Khanun (2012) | |
| CN112651463A (en) | Construction method of double-forecast model of hail weather in plateau area | |
| CN113311416A (en) | Mountain region small watershed radar quantitative precipitation estimation technology | |
| CN113866770A (en) | Hail cloud early identification method and storage medium | |
| Tridon et al. | On the realism of the rain microphysics representation of a squall line in the WRF Model. Part I: Evaluation with multifrequency cloud radar Doppler spectra observations | |
| Wu et al. | Radiance-based assessment of bulk microphysics models with seven hydrometeor species in forecasting Super-typhoon Lekima (2019) near landfall | |
| Zuo et al. | Identification of convective and stratiform clouds based on the improved DBSCAN clustering algorithm |
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 | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20181023 |