Radar quantitative precipitation dynamic classification ZI. relationship estimation method

Transcription

1 Radar quantitative precipitation dynamic classification ZI relationship estimation method Ji Miao. Perry Weather Consulting. Abstract: Using high-temporal resolution radar quantitative precipitation estimation and prediction (RQPE and RQPF) for flood monitoring and flood warning It plays an important guiding role. At present, the research methods at home and abroad mainly use probability pairing method, ZI relationship method and mathematical calculation based on ZI relationship. Method correction method, these methods have the problem of emphasizing a certain factor to ignore other factors, and for short-term heavy precipitation, especially very strong precipitation Estimated seriously. From the statistics and individual cases, the errors of four radar quantitative precipitation methods are compared and analyzed, and we are looking for a kind of radar echo. The inversion of the rate prediction field is a technical method with less error in the precipitation field, which can improve the problem of severe underestimation of short-term heavy precipitation, especially extremely strong precipitation. The method, and the method can also be implemented conveniently, quickly and accurately in business applications. Comparative study results show that: dynamic hierarchical ZI relationship The method is a better method for inverting the precipitation field of the radar QPE and the echo reflectivity prediction field, and improves the short-term heavy precipitation, especially The problem of underestimation of short-term and extremely strong precipitation of more than 55 mm/h; only 15 seconds in business calculation, it can meet the calculation result of 6 minutes. Need, and does not rely on climate statistics, easy to transplant. Keywords: RQPE; RQPF; ZI relationship; dynamic; grading

2 Introduction Radar quantitative precipitation estimation with high spatial and temporal resolution (Radar-based Quantitative Precipitation Estimate, RQPE) and Forecast (Radar-based Quantitative Precipitation Forecast, RQPF) It has an important guiding role for flood monitoring and early warning, and is often applied to earth water. Cyclic research and various hydrological and atmospheric numerical model studies, such as precipitation Research, geological hazard warning, environmental ecology, groundgas-water revenue, Precipitation predictions for land-atmosphere and atmospheric coupling models [1] and flood forecasting; RQPF research is more important, for the release of flood warnings, geological disasters The police have important instructions. RQPF technically consists of two parts: radar echo reflectivity factor Field extrapolation prediction (falling area prediction) and inversion of echo reflectivity prediction field For precipitation (precipitation inversion). At present, more research on RQPF is concerned. How to accurately predict the radar reflectivity field. For echo from echo back There is less research on the technical methods of the inversion of the report field for the precipitation field. Which scheme is necessary for precipitation inversion is very critical. This article focuses on research The latter, the inversion of the precipitation field. Radar quantitative estimation of precipitation (RQPE) and radar quantitative precipitation forecast (RQPF) on the surface, using radar reflectivity factor to estimate precipitation Quantity, in fact, RQPE is an estimate of the precipitation that has occurred (live estimation) RQPF is an estimate of the amount of precipitation that may occur in the future. (Precipitation forecast). Therefore, the RQPF calculation method can learn from the RQPE method. However, due to the different objectives of the research, the techniques and methods of research are different. Research on the use of radar for precipitation estimation has been opened at home and abroad. It has been exhibited for many years and is mainly divided into three categories: probability pairing method and ZI relationship method. And mathematical correction based on ZI relationship. The three types of methods are based on radar The observed reflectance factor Z estimates the precipitation intensity I. Probability pairing Zawadzki et al. [2], Tang et al. [3] proposed that the method assumes either The possibility of a rain intensity I is always present and the radar reflectivity factor is Z. Equal probability, according to the principle of equal probability of the two can be matched The Zi and Ii pairs of the column determine the correspondence between Z and I for each pairing statistics. system. For example, Zheng Yuanyuan et al [4] different elevation angle PPI according to different distance segments of the radar The data and the rain intensity I establish a probability pairing sequence, and then match all Z and I The linear regression regression method is used to establish the correspondence between them. Wang Yanlan et al. [5] further simplified the method and did not deliberately search for radar reflections. The relationship between the rate factor Z and the rain intensity I, but different Z and I For simple numerical correspondence, they will have an echo reflectance of 5 to 55 dbz. It is divided into 8 levels from 5 dbz, and the

3 above 8 echoes are established according to statistics. The levels correspond to 0, 2, 5, 10, 17, 25, 36 and 48 mm respectively. Precipitation value. The advantage of this approach is that it is simple to calculate and can be quickly estimated The amount of precipitation, the shortcomings are also obvious, will be some reflectivity zone It is obviously unreasonable to correspond to a limited number of rainfall values. ZI relationship method for radar estimation precipitation based on Z = ai b index relationship Calculation. ZI relationship method can be divided into several categories: fixed ZI relationship method, dynamic ZI Relationship method, classification ZI relationship method. Fixed ZI relationship law Statistics are made to determine a fixed Z = ai b index relationship and Radar precipitation estimation; dynamic ZI relationship method does not use historical statistics ZI Relationship, but use the live data to frequently establish a dynamic Z = ai b relationship; The classification ZI relationship method divides the precipitation echo properties into convective clouds, layered clouds, Different Z = ai b relationships and calculations are calculated for hail and warm rain areas. Precipitation [6-13]. Each of the above types of methods has its own characteristics, but there are emphasis on one factor. Ignore the problem of other factors, and especially strong for short-term heavy precipitation Precipitation is underestimated. The fixed ZI relationship method is simple and fast, but it is off The system cannot adapt to changes in the precipitation system and requires a large amount of historical data. To establish statistical relationships; dynamic ZI relations can adapt to the weather situation Evolution, this approach is more reasonable than using a single ZI. The disadvantage is that there is no Precipitation estimation for echo classification; classification ZI relationship method considers different The effect of type echo on ZI relationship is better, but business application It is difficult to achieve automatic division of precipitation echo types. The mathematical correction method based on the ZI relationship is through Kalman filtering, Variational correction, etc., continually approaching the precipitation to make the precipitation estimation more reasonable Think, but the calculation is relatively complicated, and the computer resources consumed are large. The party The method relies on the initial field precipitation estimation of the ZI relationship, and the ZI relationship with small error Inversion of the initial precipitation field can save the calculation time and improve the algorithm The calculation accuracy is very necessary, so the ZI relationship method with small research error is necessary. Radar quantitative precipitation forecast has many questions to be studied in business The short-term forecasting system established around the world generally contains RQPF product [14]. But like radar RQPE, there is a low level Overestimation of precipitation, serious underestimation of high-level precipitation, such as Hong Kong The "small vortex" system dynamically corrects ZI relationships using high-frequency rain gauge data To calculate RQPF [14], the test results show that the strength is greater than 50 mm / h The precipitation inversion effect is not good. In this paper, the current radar quantitative precipitation estimation and prediction algorithm is studied. The existing problems, using the radar data of Guangdong Province from 2007 to 2008, Looking for a new radar quantitative precipitation estimation scheme based on ZI relationship method, The

4 algorithm can automatically distinguish the type of precipitation echo and improve the short-term heavy precipitation. Estimated problems, and are not affected by local climate characteristics, easy to transplant; The application of radar RQPF can reduce low-level overestimation and high-level precipitation Underestimated error. 2 Data and Quality Control Two types of data are used in this paper : 1-hour precipitation observations and 3 public Highly radar CAPPI puzzle. Data collection for 2007 and 2008 The annual heavy precipitation day is carried out. Due to the strong precipitation day sample than the general precipitation day The sample is much smaller, so if you take all the general precipitation samples into the calculation Will make the statistical results tilt to a small amount of precipitation, prone to low precipitation Estimated situation. Generally speaking, there is a strong precipitation process in Guangdong, often With different magnitudes of precipitation, select a strong precipitation day as a sample, actually Take into account different levels of precipitation. This paper sets the standard for heavy precipitation days as: Guangdong Province has at least one rainfall station with daily rainfall in the radar coverage 50 mm, a total of 154 days in 2007 and 2008 meet the standard, which Some precipitation days are distributed between March and November. At present, there are more than 1,600 regional automatic stations and 86 telemetry stations in Guangdong. Station. Using the above-mentioned two types of station hourly rainfall as research data set. Quality control of rainfall data, echoing by radar, surrounding Rain and weather phenomena eliminate false data. Reliable 1 hour rain The maximum value is analyzed and the singular value is eliminated using the maximum value. Will be 1 hour The precipitation is divided into 8 orders of magnitude: 1 to 5, 5 to 15, 15 to 30, 30 to 45, 45 to 55, 55 to 65, 65 to 85, and 85 mm or more, less than 1 mm The precipitation does not participate in the statistics. After quality control, the total sample size is about 28 Million, the number of samples of each magnitude is shown in Figure 1, where 1 to 5 mm This book exceeded 210,000, accounting for the vast majority of the sample. The greater the magnitude of precipitation, The smaller the number of samples mm 5-15mm 15-30mm 30-45mm 45-55mm 55-65mm 65-85mm 85mm or more (a) Figure 1 Distribution of sample numbers of each precipitation level The radar data is taken from Guangzhou, Shantou, Meizhou, Shaoguan and Yangjiang. Doppler radar, using the km CAPPI radar Puzzle information [15]. The grid points of the puzzle are , and the starting and ending boundaries For: ~ E, 19.32~27.30 N; grid point The interval between the warp and weft is ; the interval between the puzzles is 10 bell. The radar echo before the puzzle is quality controlled and interpolated, eliminating the super

5 3 Methods Compared with the probabilistic pairing method, the ZI relation method is a physical meaning ratio A clearer estimation method, but how to choose parameters a and b in Z = ai b There are technical difficulties. Some fixed statistics in daily business a, b value, some experimentally obtained a, b value, or dynamic statistics The a, b values, resulting in a, b ranges vary greatly, take A The value ranges from 16 to 1 200, and b ranges from 1 to 2.87 [15]. In order to facilitate comparison and find the optimal solution, this paper designed 4 kinds. Radar quantitative precipitation estimation scheme to solve the problem of a and b values: fixed ZI relationship method, dynamic ZI relationship method, classification ZI relationship method and dynamics The classification ZI relationship method. In order to get a quantitative precipitation estimate for each type of radar The method is most reasonable a, b, and saves computation time, this article will a from 16 Start to at intervals of 20 (60 a values), b from 1.0 Calculate at intervals of 0.05 (38 b values) starting at 2.87 Group Z = a m I bn ( m =1, 2,..., 60; n =1, 2,..., 38), Simultaneously calculate discriminant functions [16] CTF2, when CTF2 is the smallest The Z = ai b as the optimal ZI relationship.??????? +? = i i i i i G H G H )) () (( Min 2 CTF 2, Where H i is the ZI relationship to invert the precipitation, G i is the measured precipitation, and i is the self Moving rain station sequence. However, the use of extrapolation forecasts for precipitation inversion includes extrapolation techniques. The error of surgery is not objective for measuring precipitation inversion, so this article uses Lei If the echo is replaced by the extrapolation field, the calculated radar RQPF error represents The error of the precipitation inversion technique. Inversion of echo reflectance extrapolation field The method for precipitation is similar to the radar RQPE method, the difference is radar RQPE can be corrected with live rainfall, while radar RQPF can only be used The RQPE method without rain correction is used for inversion. 3.1 Fixed ZI relationship method Using the radar reflectivity factor for the Heavy Rainfall Day And 1 hour precipitation history data, statistics get a suitable for Guangdong area The ZI relationship. The specific method is: suppose there are a total of N hours of capital Calculate group ZI relationship and CTF2 every hour, take CTF2 The smallest ZI relationship is used as the optimal ZI relationship for this time, and finally An array of optimal ZI relationships (including a and b values) of length N, pair The weighted average of a and b values??respectively obtained the fixed ZI in Guangdong Relationship, Z = I 2.23, this statistical result can be directly used in radar The RQPE and reflectance prediction fields are calculated as precipitation. Obviously The ZI relationship is fast; the disadvantage is that you need to do a lot of statistics in the early stage. Work, and due to differences in climate backgrounds, business migration is not convenient. 3.2 Dynamic ZI Relation Method The dynamic ZI relationship is based on the rapid update of data on a timely basis. Use last hour radar reflectivity data and 1 hour precipitation real-time resources Material, calculate groups Z = a m I bn ( m =1, 2,..., 60; n =1, 2,...,38) Radar RQPE and CTF2, choose CTF2 minimum ZI relationship as the optimal ZI relationship for this time; the optimal ZI for the last

6 hour Relationship application in the next hour radar echo reflectivity forecast inversion for precipitation In the field. In theory, the advantage of this method is that it does not need to be collected in the early stage. A large number of data samples are prepared for statistics, and only need to count the last hour. Optimal ZI relationship for easy porting, although calculated over a fixed ZI relationship Slow, but faster than some mathematical algorithms; lack The point is that there is no classification of precipitation types at the same time, all precipitation Types with the same a, ZI relation b values. 3.3 Classification ZI relationship method Since the precipitation intensity is closely related to the echo intensity, one cannot be used. Generic ZI relationship to calculate precipitation under different echo intensities Precipitation is classified, but convective clouds and layered clouds are distinguished in business applications. It is more difficult, so this paper takes an alternative treatment for the typing method: use Hierarchical ZI relationship, ie establishing different ZI relationships for different echo intensities Invert the precipitation. Echo reflectance from 10 to 75 at 5 dbz The interval is divided into 13 levels; according to Z = a m I bn ( m =1, 2,..., 60; n =1, 2,..., 38), for each level per hour Group of radars RQPE and CTF2, then judged according to the CTF2 minimum principle According to the selection of the optimal set of Z = a k I bk ( k =1, 2,..., 13); Have N hours of data, calculate the optimal ZI for N hours, get the order Column Z = a j, k I bj, k ( j =1, 2,..., N ; k =1, 2,..., 13); A weighted average of N hours for 13 levels of ZI relationship The final statistical classification Z = a k I bk ( k =1, 2,..., 13). Final system The scoring ZI relationship can be directly used for radar echo reflectivity prediction field Acted as a precipitation field. Statistical methods and subdivisions for different echo intensity levels The statistical principle of water type is similar, so the advantage of this method is that it is radar The precipitation on the puzzle was simply classified, but due to the use Two years of data for statistical averages, smoothing short-term and extremely strong precipitation Probability event. Figure 2 shows the variation of ZI relationship with echo reflectivity in the typing method. curve. The value of a increases with the increase of echo intensity, and the b value is between 30 and 45. The dbz (below 45 dbz) does not change much, is relatively stable, and the echo is strong Degrees greater than 45 dbz increase rapidly as the echo intensity becomes stronger. different Level echoes take different values of a and b, and describe echo enhancement to some extent. The impact on precipitation. 3.4 Dynamic Grading ZI Relationship Method Although the previous hierarchical ZI relationship considers different levels of precipitation References: [1] BRYAN C Young, ALLEN A Bradley, WITOLD F krajewski. evaluating NEXRAD multisensor precipitation estimates for operational hydrologic Forecasting[J]. J Hydrometeor, 2000, 1(3): [2] CALHEIROS RV, ZAWADZKI T J. Reflectivity rain-rate relationships for radar hydrology in Brazil[J]. Climate Apple Meteor, 1987, 26(1): 118

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