NOAA PSL OttDisdrometerStats BrushCreek for SLASH

Data Format for DisdrometerOTTParsivel Stats Header: InstrumentModel (SN: SerialNumber) Time (YYJJJHH): StatsFileBeginTime[2-digit-Year;3-digit-DayOfYear;2-digit-Hour] UTC Data Field 1: Begin time of the accumulation/averaging period in UTC MM = minute SS = second mmm = millisecond Data Field 2: End time of the accumulation/averaging period in UTC MM = minute SS = second mmm = millisecond Data Field 3-34: Partical distribution (count) binned by ClassNumber ClassNumber according to volume-equivalent diameter: ClassNumber ClassAverage(mm) ClassSpread(mm) 1 0.062 0.125 2 0.187 0.125 3 0.312 0.125 4 0.437 0.125 5 0.562 0.125 6 0.687 0.125 7 0.812 0.125 8 0.937 0.125 9 1.062 0.125 10 1.187 0.125 11 1.375 0.250 12 1.625 0.250 13 1.875 0.250 14 2.125 0.250 15 2.375 0.250 16 2.750 0.500 17 3.250 0.500 18 3.750 0.500 19 4.250 0.500 20 4.750 0.500 21 5.500 1.000 22 6.500 1.000 23 7.500 1.000 24 8.500 1.000 25 9.500 1.000 26 11.000 2.000 27 13.000 2.000 28 15.000 2.000 29 17.000 2.000 30 19.000 2.000 31 21.500 3.000 32 24.500 3.000 Note: Class 1 and Class 2 are limits and are not evaluated at the current time in measurements using the Parsivel since they are outside the measurement range of the device. Data Field 35-37: Data acquisition software quality control Blackout = number of data samples excluded during PC clock synchronization Good = number of samples that passed the quality control checks, as performed by the data acquisition software Bad = number of samples that failed the quality control checks, as performed by the data acquisition software Data Field 38-42: Precipitation statistics NumParticle = total number of detected particles Rate(mm/h) = rain rate; units: millimeter per hour Amount(mm) = interval rain accumulation ; units: millimeter AmountSum(mm) = event rain accumulation; units: millimeter Z(dB) = radar refelctivity factor; units: decibel Data Field 43-48: Laser status NumError = number of sample instances that were reported as dirty, very dirty, or damaged Dirty = laser protective glass is dirty, but measurements are still possible VeryDirty = laser protective glass is dirty, partially covered; no further usable measurements are possible Damaged = laser damaged SignalAvg = average signal amplitude of the laser strip; unitless SignalStdDev = standard deviation of the signal amplitude of the laser strip; unitless Data Field 49-54: Sensor status TempAvg(C) = average sensor temperature; units: Celsius TempStdDev(C) = standard deviation of the sensor temperature; units: Celsius VoltAvg(V) = sensor power supply voltage; units: Volts VoltStdDev(V) = standard deviation of the sensor power supply voltage; units: Volts HeatCurrentAvg(A) = average heating system current; units: Amps HeatCurrentStdDev(A) = standard deviation of the heating system current; units: Amps Data Field 55-58: Precipitation partitioning NumRain = number of particles detected as rain NumNoRain = number of particles detected not as rain NumAmbig = number of particles detected as ambiguous Type = precipitation type (1=rain; 2=mixed; 3=snow) Note: NumRain, NumNoRain, and NumAmbig counts are determined based on size-velocity masking described in: Yuter, S. E., D. E. Kingsmill, L. B. Nance, and M. Loffler-Mang, 2006: Observations of precipitation size and fall speed characteristics within coexisting rain and wet snow. J. Appl. Meteor.,45, 1450-1464. Precipitation type is determined by RainFraction thresholds, where RainFraction = NumRain / (NumRain + NumNoRain), and Type = 1 (rain) for RainFraction > 0.95 Type = 3 (snow) for RainFraction < 0.05 Type = 2 (mixed) for RainFraction >= 0.05 and RainFraction <= 0.95 Erroneous particle-size-velocity measurements (and thereby derived precipitation types) can occur at higher wind speeds (as low as ~ 6 m/s), as documented in: Neiman, P.J., D.J. Gottas, A.B. White, W.R. Schneider, and D. Bright, 2018: A Real-Time Online Data Product that Automatically Detects Easterly Gap Flow Events and Precipitation Type in the Columbia River Gorge. J. Atmos. Oceanic Technol., 35, 2037-2052. Less conservative RainFraction thresholds for precipitation typing were also explored in Neiman et al.DisdrometerOTTParsivel 统计数据集格式 文件头：仪器型号（SN：序列号（Serial Number）） 时间格式（YYJJJHH）：统计文件起始时间，格式为[两位年份; 三位一年中日序数; 两位小时]，采用协调世界时（UTC） 数据字段1：累积/平均时段的UTC起始时间 单位说明：MM=分钟，SS=秒，mmm=毫秒 数据字段2：累积/平均时段的UTC结束时间 单位说明：MM=分钟，SS=秒，mmm=毫秒 数据字段3~34：按档号（ClassNumber）分档的粒子数分布（计数）。档号基于体积等效直径划分，对应关系如下： 1 0.062 0.125 2 0.187 0.125 3 0.312 0.125 4 0.437 0.125 5 0.562 0.125 6 0.687 0.125 7 0.812 0.125 8 0.937 0.125 9 1.062 0.125 10 1.187 0.125 11 1.375 0.250 12 1.625 0.250 13 1.875 0.250 14 2.125 0.250 15 2.375 0.250 16 2.750 0.500 17 3.250 0.500 18 3.750 0.500 19 4.250 0.500 20 4.750 0.500 21 5.500 1.000 22 6.500 1.000 23 7.500 1.000 24 8.500 1.000 25 9.500 1.000 26 11.000 2.000 27 13.000 2.000 28 15.000 2.000 29 17.000 2.000 30 19.000 2.000 31 21.500 3.000 32 24.500 3.000 注：当前使用Parsivel激光雨滴谱仪进行测量时，第1、2档超出仪器测量范围，暂不参与计算。 数据字段35~37：数据采集软件质量控制指标 Blackout：PC时钟同步期间被排除的采样样本数 Good：通过数据采集软件质控检查的采样样本数 Bad：未通过数据采集软件质控检查的采样样本数 数据字段38~42：降水统计指标 NumParticle：总检测粒子数 Rate(mm/h)：降雨率，单位：毫米每小时 Amount(mm)：时段降雨累积量，单位：毫米 AmountSum(mm)：事件降雨累积量，单位：毫米 Z(dB)：雷达反射率因子，单位：分贝 数据字段43~48：激光状态指标 NumError：被标记为脏污、严重脏污或损坏的采样实例数 Dirty：激光保护玻璃脏污，但仍可进行测量 VeryDirty：激光保护玻璃严重脏污、部分遮挡，无法进行有效测量 Damaged：激光损坏 SignalAvg：激光条平均信号幅度（无量纲） SignalStdDev：激光条信号幅度的标准偏差（无量纲） 数据字段49~54：传感器状态指标 TempAvg(℃)：传感器平均温度，单位：摄氏度 TempStdDev(℃)：传感器温度标准偏差，单位：摄氏度 VoltAvg(V)：传感器供电电压平均值，单位：伏特 VoltStdDev(V)：传感器供电电压标准偏差，单位：伏特 HeatCurrentAvg(A)：加热系统平均电流，单位：安培 HeatCurrentStdDev(A)：加热系统电流标准偏差，单位：安培 数据字段55~58：降水分型统计 NumRain：检测为降雨的粒子数 NumNoRain：检测为非降雨的粒子数 NumAmbig：检测为模糊类的粒子数 Type：降水类型（1=降雨；2=混合型；3=降雪） 注：NumRain、NumNoRain及NumAmbig的计数基于Yuter等人2006年提出的粒径-速度掩膜法确定，相关文献如下： Yuter, S. E., D. E. Kingsmill, L. B. Nance, and M. Loffler-Mang, 2006: 共存降雨与湿雪条件下的降水粒径及落速特征观测. *Journal of Applied Meteorology*, 45, 1450-1464. 降水类型通过降雨分数（RainFraction）阈值确定，其中： RainFraction = NumRain / (NumRain + NumNoRain) 当RainFraction > 0.95时，Type=1（降雨）； 当RainFraction < 0.05时，Type=3（降雪）； 当0.05 ≤ RainFraction ≤ 0.95时，Type=2（混合型降水）。 另有研究表明，在较高风速（低至约6 m/s）条件下，可能出现错误的粒子粒径-速度测量结果，进而导致降水分型误差，相关文献如下： Neiman, P.J., D.J. Gottas, A.B. White, W.R. Schneider, and D. Bright, 2018: 一种可自动检测哥伦比亚河峡谷东风间隙流事件与降水类型的实时在线数据产品. *Journal of Atmospheric and Oceanic Technology*, 35, 2037-2052. Neiman等人的研究中还探索了更为宽松的降雨分数阈值用于降水分型。