精确测量雨滴尺寸分布（DSD）是降水微物理研究和定量降水估算的基础，然而广泛使用的OTT Parsivel-2散射计在复杂地形条件下存在系统性偏差，影响数据可靠性。本数据针对中国西北部六盘山地区，通过2022-2023年5月至8月期间同步开展的 Parsivel² –2DVD观测项目，建立了经过校准的 DSD 数据集。采用两阶段迭代校准方法，将各直径等级的降雨速度调整至理论值，并针对四种降雨强度区间优化了浓度校正矩阵P(i)。该校准显著提升了 DSD 矩估计精度：第0阶矩的归一化绝对误差（NAE）从82%降至2%，第4阶矩的相关系数（CC）从0.87提升至0.99。通过对五个站点网络两年来的雨量计观测数据进行独立验证，证实了该方法的有效性。\n
该数据集采用NetCDF4格式，遵循CCBY4.0许可协议，包含分辨率为1分钟的 DSD 光谱数据，包含了分档后降水雨滴下落末速度和雨滴浓度信息，可支持降水微物理特性分析、雷达定量降水估算、数值天气预报以及地形复杂区域的水文模拟。\n
本数据提供了 Python 校准代码示例，可自动完成数据读取。数据文件命名规则：CX202206211931_2030.nc，其中CX 为站点代码（LP/KT/CX/PY/ZY），20220621为年月日，1931_2030为时间区间，分辨率（1min）- 详细变量说明和单位参见数据包中的 README 文件。", "ds_source": "", "ds_process_way": "", "ds_quality": "", "ds_acq_start_time": "2022-05-01 00:00:00", "ds_acq_end_time": "2023-08-31 00:00:00", "ds_acq_place": "中国六盘山地区", "ds_acq_lon_east": 108.0, "ds_acq_lat_south": 34.5, "ds_acq_lon_west": 105.5, "ds_acq_lat_north": 36.5, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 31583586, "ds_files_count": 0, "ds_format": "NetCDF4", "ds_space_res": "", "ds_time_res": "日", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "c96a135d-7ace-4a83-82a5-104dac71c9dd.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "None", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": null, "ds_serv_phone": null, "ds_serv_mail": null, "doi_value": "", "subject_codes": [ "170" ], "quality_level": 0, "publish_time": "2026-06-15 09:57:46", "last_updated": "2026-06-15 09:57:46", "protected": false, "protected_to": "2026-12-09 00:00:00", "lang": "zh", "cstr": "11738.11.ncdc.nieer.db7453.2026", "i18n": { "en": { "title": "A Calibrated Raindrop size distribution dataset (2022-2023) from Liupan Mountains Precipitation Observational Network, Northwestern China", "ds_format": "NetCDF4", "ds_source": "", "ds_quality": "", "ds_ref_way": "", "ds_abstract": "
Accurate measurement of raindrop size distribution (DSD) is the basis for precipitation microphysics research and quantitative precipitation estimation. However, the widely used OTT Parsivel-2 scatterometer has systematic deviations under complex terrain conditions, which affects data reliability. We have established a calibrated DSD data set for the Liupanshan area in northwest China through the Parsivel²-2DVD observation project simultaneously carried out from May to August 2022. A two-stage iterative calibration method was used to adjust the rainfall speed of each diameter level to theoretical values, and the concentration correction matrix P(i) was optimized for four rainfall intensity intervals. This calibration significantly improves the accuracy of DSD moment estimation: the normalized absolute error (NAE) for the 0th moment is reduced from 82% to 2%, and the correlation coefficient (CC) for the 4th moment is increased from 0.87 to 0.99. The effectiveness of the method was confirmed by independent verification of rain gauge observation data from five station networks over the past two years.\r\n
The dataset adopts NetCDF4 format and follows the CCBY4.0 license agreement. It contains DSD spectral data with a resolution of 1 minute. It contains information on the final falling velocity and raindrop concentration of precipitation after classification. It can support precipitation microphysical characteristics analysis, radar quantitative precipitation estimation, numerical weather prediction, and hydrological simulation in areas with complex terrain.\r\n
We provide a Python calibration code example that automatically completes data reading. Naming rule for data files: CX202206211931_2030.nc, where CX is the site code (LP/KT/CX/PY/ZY), 20220621 is the month, and 1931_2030 is the time interval, resolution (1min)-refer to the README file in the data package for detailed variable descriptions and units.", "ds_time_res": "", "ds_acq_place": "Liupan Mountain Area, China", "ds_space_res": "", "ds_projection": "", "ds_process_way": "", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "belong_to_nieer": false, "ds_topic_tags": [ "液滴尺寸分布", "雨滴谱校准", "Parsivel-2", "二维视频散射仪", "六盘山" ], "ds_subject_tags": [ "地球科学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国西北", "六盘山" ], "ds_time_tags": [ 2022, 2023 ], "ds_contributors": [ { "true_name": "赵果", "email": "guozh@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "" } ], "ds_meta_authors": [ { "true_name": "赵果", "email": "guozh@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "" } ], "ds_managers": [ { "true_name": "赵果", "email": "guozh@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "" } ], "category": "气象" }