Improving the use of ground weather radar for monsoon rainfall estimation

Weather radar can offer synoptic measurement at a higher temporal and spatial resolution to extract the rain information. Rainfall can be inverted from the radar reflectivity using the power-law relation to ground rain gauge measurement. The relationship known as Z-R model has been established in many variants but the uncertainty from the sampling bias and the Z-R variability of single-polarization radar observation on monsoon rain becomes subject to research. The inconsistence of the Z-R model exemplifies the main disadvantages of radar rainfall estimation. Previously proposed universal Z-R models are inadequate to be applied for heterogenous rain intensity during monsoon seasons . Studies on the accuracy of Z-R model are lacking for many reasons such as complex radar data structure, requires a large data storage and is very expensive when used for long-term observations. Radar reflectivity data was collected by the S-band single-horizontal polarization Doppler radar at Kota Bahru, Kelantan and the data is managed by Malaysia Meteorological Department (MMD). The observation covers radial measurement from 50 to 250 km radii. The reflectivity data represents the return signal sampled in 10 minutes and at position within 2-km altitude from the ground which was generated from the lowest scanning angle of 0.7 degree. Such setup would reduce the ground clutter effect which is typically pronounced in the signal received close to the radar antenna providing reliable rainfall information near to the surface and also minimizing the temporal uncertainty in the Constant Plan Position Indicator (CAPPI). Calibration through the threshold log receiver of signal-to-noise ratio (LOG) and the Doppler channel clutter-to-signal ratio (CSR) were applied in pre-processing for minimizing the uncertainties formed by the beam blockage, ground clutter, and during instrument calibration (VAISALA 2016). The radar data conversion from SIGMET to Netcdf was applied through Python Atmospheric Radiation Measurement (ARM) Radar Toolkit (Py-ART). Tipping bucket rain gauges provide rain intensity at a volumetric resolution of 0.2 mm in 1-hour resolution, organised by the Department of Irrigation and Drainage (DID). For this study, there are 58 gauges involved in the 200-km radar observation range. These studies reported that 50 gauges have experienced more than 10% of data void and 41 gauges were working in homogenous fashion while the rest of gauges were doubtful. The digital elevation model (DEM) data is a product of The Shuttle Radar Topography Mission (SRTM) with 30 meters (1 arc-second) pixel spacing and was used for altitude correction of each rain gauge. The DEM products have been corrected on the void pixels they can be downloaded from the U.S. Geological Survey (USGS) website. Reflectivity and rain gauge data are obtained during the wet season of the northeast monsoon from 2013 to 2015 (January, February, March, September, October, November and December).气象雷达可提供更高时空分辨率的天气监测数据，用以提取降水信息。通过与地面雨量筒观测数据建立的幂律关系，可由雷达反射率反演得到降水量。这类被称为Z-R模型的关系已有多种变体被提出，但针对季风降水的单极化雷达观测中，由采样偏差及Z-R关系变异性带来的不确定性仍有待深入研究。Z-R模型的不一致性正是雷达降水估算的主要弊端所在。此前提出的通用Z-R模型难以适用于季风季的非均匀降水强度场景。由于雷达数据结构复杂、存储需求大且长期观测成本高昂等诸多原因，目前针对Z-R模型精度的研究仍较为匮乏。 本次研究使用的雷达反射率数据由位于吉兰丹州哥打巴鲁的S波段单水平极化多普勒雷达采集，数据由马来西亚气象局（Malaysia Meteorological Department, MMD）管理。该观测的径向探测范围为50至250千米。反射率数据为每10分钟采样一次的回波信号，采集高度距地面2千米以内，由0.7度的最低扫描仰角生成。该设置可削弱通常在雷达天线附近接收信号中较为显著的地面杂波效应，既能获取靠近地表的可靠降水信息，同时也能降低恒定平面位置显示器（Constant Plan Position Indicator, CAPPI）的时间不确定性。 预处理阶段通过信噪比阈值对数接收机（LOG）及多普勒通道杂散信号比（CSR）进行校准，以减小波束遮挡、地面杂波以及仪器校准过程中产生的不确定性（VAISALA，2016）。研究采用Python大气辐射测量（Python Atmospheric Radiation Measurement, ARM）雷达工具包（Py-ART）将SIGMET格式的雷达数据转换为NetCDF格式。翻斗式雨量筒由马来西亚灌溉与排水部（Department of Irrigation and Drainage, DID）布设，其降水强度测量的体积分辨率为0.2毫米，时间分辨率为1小时。本次研究的雷达观测范围为200千米，共涉及58个雨量筒。相关研究表明，其中50个雨量筒存在超过10%的数据缺失，41个雨量筒运行状态均匀可靠，其余雨量筒的运行状态存疑。 数字高程模型（Digital Elevation Model, DEM）数据由航天飞机雷达地形测绘任务（Shuttle Radar Topography Mission, SRTM）提供，像素间距为30米（1弧秒），用于对每个雨量筒进行高程校正。该DEM产品已针对缺失像素进行了校正，可从美国地质调查局（U.S. Geological Survey, USGS）官网下载。本次研究的反射率数据与雨量筒观测数据均采集于2013至2015年的东北季风湿季（1月、2月、3月、9月、10月、11月及12月）。