Póster presentado en: 3rd European Nowcasting Conference, celebrada en la sede central de AEMET en Madrid del 24 al 26 de abril de 2019

Jang, Kun-Il

Kim, Ji-Won

Lee, Jeungwhan

Nam, Kyung-Yeub

Oh, Young-A

Son, MyoungJae

English

Agencia Estatal de Meteorología

(BRI) (YIT) (GDK) (KWK) (GNG) (KSN) (JNI) (GSN) (SSP) (MYN) (PSN) Very Short-term Forecasting of Precipitation Based on Hybrid Surface Rainfall Technique in Korea MyoungJae SON, Young-A OH, Ji-Won KIM, Kyung-Yeub NAM, Kun-Il JANG, and Jeungwhan LEE  Weather Radar Center  /  Korea Meteorological Administration   P9 Introduction Summary and future works  Summary :      - Improvement of radar-QPF : HSR + raingauge adjustment      - Nowcasting of rainfall by combined MAPLE with improved radar-QPE   Future works :      - Evaluation of QPF in 2018 annual data      - Improvement of  prediction quality Methodology  KMA (Korea Meteorological  Adinistration) has utilized MAPLE technique as a nowcasting tool of precipitation and lightning  up to 6 hours for  a routine  forecasting operation since 2008.  Recently,  we have developed the  advanced radar-QPE technique (Hybrid Surface Rainfall, HSR) and applied raingauge adjustment technique  based on multi-quadratic interpolation.   In this study, we  aim to  improve  the performance of operational short-term  forecasting  technique by combined  MAPLE with two techniques and to evaluate  its performance quantitatively. W R C Weather  Radar  Center Acknowledgements This research is supported by "Development and application of Cross governmental dual-pol radar Harmonization (WRC-2013-A-1)" project of the Weather Radar Center, Korea Meteorological Administration. Evaluation Corrected Rainfall   Period: 1st  Aug.  2018  ~  31nd  Oct.  2018    - 2x2 Contingency Table   : N = H (Hit)  +  M (Miss)  +  F (False Alarm)  +  C (Correctional reject) - Bias (Bias score)                                                         - POD (Probability Of Detection)  - FAR (False Alarm Ratio)                                           - CSI (Critical Success Index) PREDICTION YES NO OBSERBATION YES H M NO F C - ME (Mean Error)                                                            - MAE (Mean Absolute Error)  - RRMSE (Relative Root Mean Square)                                         - CORR (Correlation coefficient)   1) QPE  Rainfall Estimation Technique based on Hybrid Surface    - the rainfall estimation technique based on the lowest-observable elevation surface that immune to radar        beam blockage,  ground clutter contamination and non-meteorological echoes in radar volume file ＜AWS network in Korea＞ AWS HSR Rainfall < Procedure >  Step1)   INPUT : HSR / AWS rainfall Step2)   Derive a variance field of AWS Rain Rate and radar rainfall estimation Step3)   Objective analysis : Variance field  derived by multi-quadric interpolation Step4)   Adjustment of radar-QPE by using variance field Step5)   Objective analysis :  AWS rainfall Step6)   Merge between Adjusted radar-QPE and AWS rainfall field  Post -processing of QPE : Raingauge Adjustment   - Adjusted by using spatial error distribution (variance) between raingauge rainfall and radar-QPE Korea Meteorological Administration QPE QPF Motion vector field HSR AWS Rainfall field (10 min interval) 1hr cumulative rainfall Lightning density Lightning strikes probability Lightning data  Nationwide Weather Radar Network    -  KMA(10) : 9 S-band Dual-Polarization radars + 1 single-polariztion radar     -  Ministry of Environment(6) : 6 S-band Dual-Polarization radars   Automatic Weather Station (AWS)     -  Dense AWS network  : 854 raingauge (1 min interval update)  Data Weather Radar Network of KMA : 15 radars - Operational: 10 S-band radar - Research: 4 radars (S-band: 1, X-band: 3) - Airport: 1 TDWR (C-band, Doppler radar) TDWR            1 Research       4 Operational   10  Flow chart of nowcasting procedure Before After 𝑡0 𝑡−1 𝑡−2 Assimilation Window forecasts ∆𝑡 ∆𝑡 Motion vector field  VET(Variational Echo Tracking)   - Determine a radar echo motion vector field by minimizing cost function   - Divided into sub-domains and compute velocity vector for each sub-area by minimizing         reflectivity difference or cost function between two composite maps by a certain time interval : Reflectivity constraint : Smoothness penalty   - To solve the minimizing cost function,        a conjugate-gradient algorithm was used in the global minimization of the cost function.   - To avoid converging toward secondary minima, use the scaling-guess procedure at least twice times.  2) QPF   Semi-Lagrangian backward advection   - Generate future rainfall field by semi-Lagrangian backward scheme   - Advantage : allows for differential motion during the forecasting process compared with single or constant  velocity                              vector simulating rotation at the near synoptic scale of the composite radar maps Semi-Lagrangian Linear extrapolation P O - Use backward scheme : to avoid ‘holes’ in forecast map     because of a region of divergence       : Linear advection       : Semi-Lagrangian advection  : displacement vector,   : velocity field,   : lead time,   : initial time,   : position at the grid point P,   : time length         ME : Temporal trend of ME was similar in 1hour,                    While difference increase after 1 hour         CSI : rapidly decreases within 1hours,  while slowly decrease after 1hour                   Temporal trend of CSI was similar between 10 min and 60 min  

Very short-term forecasting of precipitation based on hybrid surface rainfall technique in Korea

https://repositorio.aemet.es/bitstream/20.500.11765/10657/1/P09_Son_3ENC2019.pdf

Very short-term forecasting of precipitation based on hybrid surface rainfall technique in Korea

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