The Southern China Monsoon Rainfall Experiment (SCMREX) - Progress in Jan-July 2017
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1 The Southern China Monsoon Rainfall Experiment (SCMREX) - Progress in Jan-July 2017 A WMO World Weather Research Program (WWRP) Research and Development Project (RDP) Leading scientist: Dr. Yali LUO Chinese Academy of Meteorological Sciences
2 Southern China Monsoon Rainfall Experiment (SCMREX, ) - A WMO/WWRP RDP Topography and 800-, 1000-mm contours of seasonal-mean Rainfall (mm) in Apr-Jun, Luo, Zhang, Wan, et al., 2017, Bull. Amer. Meteor. Soc. Scientific Objectives With new, integrated data collected during the IOPs, To better understand development of the heavy-rain-producing storms in Southern China during the presummer rainy season Processes governing convective initiation & development Storm-internal processes To improve QPF skill by better understanding multi-scale precipitation processes DA impact study, model physics scheme improvement, and ensemble experiments at convection-permitting scale 2
3 IOPs: 1 May to mid-jun, /650/624/391 extra soundings in 2014/15/16/17 IOPs 8 radiosonde stations Field Campaign Participants State Key Laboratory of Severe Weather (LaSW), Chinese Academy of Meteorological Sciences (CAMS) Institute of Tropical and Marine Meteorology, CMA, Guangzhou (ITMM) Institute of Heavy Rain, CMA, Wuhan (IHR) Nanjing University (NJU) Chengdu University of Information Technology (CUIT) Hong Kong Observatory (HKO) Guangdong Meteorological Bureau (5 operational S-POLs) Guangxi Meteorological Bureau Hainan Meteorological Bureau 3
4 About 60 participants China, US, UK, Japan, India, Malaysia, Hong Kong, PSA 31 Oral Presentations Instruments & data Physical mechanism NWP
5 2017 Field Campaign New capability: Remote monitoring of the instruments Longer IOP Started in mid-march, some instruments still running Enhanced radiosonde observation during 1 May-15 June Stations: Guangdong 3, Guangxi 1, Hong Kong 1 Convective scale Gulf of Tonkin Mesoscale Synoptic scale South China Sea
6 Three super sites: Cloud & precipitation (Longmen) Fast-evolving storms (Nanhai) Lightning (Conghua) Major Instruments Dual-Polarization Radar (1 X-POL, 2 C-POLs, 5 fixed S-POLs) C-band frequency-modulated continuous-wave radar (C-FMCW) Ka-band Cloud Radars (CRs) Micro Rain Radar (MRR) Microwave Radiometer (MR) 2-D Video Disdrometers (2DVD) Laser-optical Disdrometer Laser Ceilometer (Ceilometer) X-band Phased-array Radar (X- PAR) Total-sky Cloud Imager (TCI) Precipitation Particle Imager (PPI) Cloud Condensation Nuclei Counter (CCNC) Aerodynamic Particle Sizer (APS) Lightning Low-frequency E-field Detection Array (LFEDA)
7 Publication List (I) Bao, X., Y. Luo, et al., Assimilating Doppler Radar Observations with an Ensemble Kalman Filter for Convection-permitting Prediction of Convective Development in a Heavy Rainfall Event during the Pre-summer Rainy Season of South China. Sci. China, in press. Furtado, K., P. R. Field, et al., Evaluation of the Effects of Cloud-Microphysics on Convection-permitting Simulations of Heavy Rainfall over South China during SCMREX. Quart. J. Roy. Meteor. Soc., To be submitted. Huang L. and Y. Luo, Evaluation of Quantitative Precipitation Forecasts by TIGGE Ensembles for South China during the Pre-summer Rainy Season. J. Geophys. Res., accepted. Luo, Y., R. Zhang, et al., The Southern China Monsoon Rainfall Experiment (SCMREX). Bull. Amer. Meteor. Soc., 98, Luo, Y., Advances in Understanding the Early-Summer Heavy Rainfall over South China. Invited review chapter in The Global Monsoon System, III, a WMO Quadrennial Review. Editors: C.P. Chang, N. G. Lau, R. H. Johnson, B. Wang and M. Wheeler. Vol. 9, World Scientific Series on Asia-Pacific Weather and Climate, Wang, B., et al., Investigation of microphysical properties using C-band dualpolarimetric radar measurements in several heavy rainfall cases over South China. J. Geophys. Res., To be submitted.
8 Publication List (II) Wu, C., L. Liu, et al., Statistics-based Optimization of the Polarimetric Radar Hydrometeor Classification Algorithm and Its Application for a Squall Line in South China. Adv. Atmos. Sci., in press. Jiang, et al., Diurnal Variations of Pre-Summer Rainfall over Southern China. J Climate. Luo, Y., M. Wu, et al., 2016: Seasonality and synoptic situations of extreme hourly precipitation over China. J. Climate, 29, Wu, M. and Y. Luo, Mesoscale Observational Analysis of Lifting Mechanism of a Warmsector Convective System Producing the Maximal Daily Precipitation in China Mainland during the First Rainy Season of J. Meteor. Res., 30(5), Zhang, X., et al., Impact of Assimilating Wind Profiling Radar Observations on Convection-permitting Quantitative Precipitation Forecasts during SCMREX. Weather Forecast. DOI: Xia, R., et al., A 6-Year Cloud-to-Ground Lightning Climatology and Its Relationship to Rainfall over Central and Eastern China. J Appl. Meteor. Clim. doi: /jamc-d Zhao, et al., Severe rainfalls in South China during May 2013 and its relation to the onset of the South China Sea summer monsoon. Acta Meteor. Sinica, 73, Wang, H., Y. Luo, B. Jou, Initiation, maintenance, and properties of convection in an extreme rainfall event during SCMREX. J. Geophys. Res. 119, 13,206 13,232, doi: /2014jd022339
9 An Optimization Method of the Hydrometeor Classification Algorithm for Polarimetric Radars in South China (Liping LIU and Chong WU, CAMS) Z ρ hv HCA from the U.S is not suitable for Chinese radars Specialized QC procedures (calibration, noise filtering ) Statistics-based Optimization for parameters of membership functions (MFs), thresholds of confidence vector, other subalgorithms Independent MFs for each polarimetric radar in South China (Wu et al., Adv. Atmos. Sci., 2017, in press) Dry snow, crystals Wet snow Rain Signature of melting layer ρ hv Z Step 1: melting layer detection Step 2: data statistics Step 3: new parameters and MFs 0 1 ρ hv Z
10 Merging the observations from a Ka-Band Cloud Radar, C-Band Frequency Modulated Continuous Wave Radar, and a Ceilometer (Liping LIU, CAMS) CR1 Reflectivity Reflectivity Merged Data CR2 Velocity CR3 FMCW Data resource
11 A statistical analysis of cloud-precipitation characteristics using the merged data (Liping LIU, CAMS) Diurnal variation of radar echo occurrence freq. (a) Normalized CFAD High-level non-precipitating Clouds Mean Reflectivity (a) Low-level Mid-level High-level H (km) 8.0 Mid-level non-precipitating Clouds Low-level non-precipitating Clouds Z(dBZ) 16.0 (b) Non-precipitating Clouds Stratiform Convective Convective precipitating Clouds H (km) Precipitating Clouds Stratiform precipitating Clouds Z(dBZ)
12 Observation of lightning activity in South China (Weitao LU, CAMS) Lightning Low-Frequency E-field Detection Array (LFEDA) is developed to locate the 3-D positions of the lightning discharge events. The LFEDA shows fine performance with its location efficiency and location error for the return strokes of triggered lightning flashes being 95% and 102 m, respectively. The LFEDA is capable of describing the three-dimensional channel of lightning to some extent. The LFEDA provide much more abundant information on the lightning activity than the operational lightning location system. 3-D lightning activity in two thunderstorm processes Geographical layout of LFEDA sensors Shi, D., et al., 2017: Low-Frequency E-field Detection Array (LFEDA) construction and preliminary results. Sci. China Earth Sci., in press. 3-D location of an IC flash 3-D location of a CG flash
13 Urban Extreme Rainfall (Yali LUO, CAMS) Numerical experiments (500m grid spacing) A 4D variational Doppler radar analysis system (VDRAS) Nocturnal convective initiation Megacity, local topography, LLJS with ample moisture Unique storm structure A moderately-deep meso-γ-scale circular convective core (C 3 ) within a meso-β-scale vortex; a moist absolutely unstable layer in C mm/h AWS Observations Surface wind + T Accumulated rain 524 mm Meso-vortex VDRAS analysis MAUL in C3 CFAD of polarimetric variables DSD retrieval disdrometer Dual-pol radar
14 An Extreme Rainfall Event Associated with a Mesoscale Outflow Boundary and Multiple Rainbands (Yali LUO, CAMS + Xi LIU, NUIST) Land/sea frictional contrast, a pre-existing cold pool, topographical lifting early CI Lifting of near surface oceanic southerly airflow by the quasi-stationary MOB Echo-training aided by the larger-scale southwesterly flow Forward (eastward) rainband building Composite reflectivity 13:41-13:56 BST Dual-Doppler wind retrieval
15 Effects of Cloud-Microphysics on Convectionpermitting Simulations of Heavy Rainfall (Kalli Furtado, Met Office, UK) Met Office Unified Model The relative importance of the number of prognostic moments is assessed, in comparison to cloudfraction diagnosis and the microphysical properties of hydrometeor particles. The information is used to improve the performance of a single-moment scheme make judgments about the cost-to-benefit of higher-order microphysics schemes Radar Double moment Single moment Single moment +modified Nr
16 Impact of radar radial velocity data assimilation on probabilistic forecasts (Bao er al., Sci. China, 2017) (Xinghua BAO, Yali LUO, CAMS & Jiaxiang SUN, CAAC & Zhiyong MENG, PKU & Jian YUE, NMC) The radar DA greatly improves the probability forecast of MCS over the land and MCS offshore. The finding supports the effectiveness of DA and the advantage of convection-permitting ensemble forecasting for predicting convection development over South China. NODA 60-member ensemble forecasts Radar DA 60-member ensemble forecasts 0h +1h +2h +3h 0h +1h +2h +3h +4h +5h +6h +7h +4h +5h +6h +7h Neighborhood ensemble probability of 15dBZ radar reflectivity (shading); Observed 15 dbz boundary (solid line)
17 Convection-Permitting Ensemble Prediction Study (Xubin ZHANG, ITMM) Progress: A CP Ensemble Prediction System (EPS) was established on Tianhe2 supercomputer and showed encouraging results on QPF during the half-month batch experiment (8-23 May, 2014) Plan: To investigate the impacts of various perturbation methods on the CPEPS performance. FSS > 20 mm h km PM PMnew OPT ctl ~3 0~6 0~12 Valid (h)
18 Analysis of the TIGGE ensembles (Ling HUANG & Yali LUO, CAMS) Huang and Luo (2017, JGR accepted) Evaluation of 5 global EPSs seasonal-average medium-range prediction in four representative cases Key synoptic factors synoptic analysis & ESA low-level southwesterly flow & moisture ECMWF CMA (displacement) KMA NCEP JMA (underestimation)
19 Thanks for your attention!
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