Hourly SNOTEL Data

This workflow build on STAR★Methods published in Heggli et al., 2022 (https://doi.org/10.1016/j.isci.2022.104240). The data and code published here is free to use, but please cite the original paper with the peer-reviewed STAR★Methods when using the data or this code. This code reflects improvements to the original code and was presented at 2023 Western Snow Conference. Level 3 hand-cleaned data produced by Anne Heggli with these methods can be found in this repository. PRIOR TO USE PLEASE UNDERSTAND: The temperature parameter incorporates the NOAA9 9th order polynomial bias correction issued by the NRCS in the Level 1 data process. The code includes an experimental precipitation QC process that should be used with an abundance of caution as precipitation gauges experience snow plugs that can result in entire snowfall events being missed for days to weeks on end. This is an evolving workflow that is a step towards improving the quality of hourly SNOTEL data. I do expect to improve upon these methods in the coming years and I hope that others will collaborate to improve the code and process so that we may all be able to make use of the rich data set. This data process is not an official product of the NRCS and should not be reflected as one in any research. It is just one method to make use of the SNOTEL network data. HOW TO USE THE CODE: These files are designed to be used consecutively. First, use the DownloadSNOTEL.py file to download hourly and daily SNOTEL data from the stations and water years of interest. Second, use SNOTEL_L0.py file to remove the daily QC'd value in the midnight stamps. Third, use SNOTEL_L1.py followed by SNOTEL_L2.py file to automate the Level 1 and Level 2 process. If you wish to clean the data manually (Level 3), don't hesitate to get in touch with [e-mail] for the Level 3 process. I am happy to share and help, but it is a bit complicated so I would prefer to walk anyone through these methods if they would like to use them. QA & QC FLAGS: Flag : Name : Description Quality Assurance (QA) Flags R : Raw : No Human Review F : Flagged : Automated QC Flag Assigned P : Provisional : Preliminary Human Review A : Approved : Processing and Final Review Completed Quality Control (QC) Flags V : Valid : Valid observed value E : Edit : Edit existing value S : Suspect : Suspect value

本工作流基于Heggli等人2022年发表的STAR★方法（STAR★Methods）构建，原文DOI链接为https://doi.org/10.1016/j.isci.2022.104240。本仓库发布的数据与代码可免费使用，但在使用该数据或代码时，请务必引用经同行评议的STAR★方法原文。

本代码相较于原始代码进行了优化改进，并曾在2023年西部雪科学会议（Western Snow Conference）上展示。采用此方法由Anne Heggli制作的3级手动清洗数据可在本仓库中获取。

【使用前请知悉】
温度参数整合了美国自然资源保护局（Natural Resources Conservation Service, NRCS）在1级数据处理流程中发布的NOAA9阶多项式偏差校正方案。本代码包含一项实验性降水质量控制流程，使用时需格外谨慎：降水测量仪器易遭遇雪堵，可能导致数天乃至数周的降雪数据完全缺失。

本工作流仍在持续迭代优化，旨在提升SNOTEL（Snow Telemetry，雪遥测网络）逐小时数据的质量。本人预计未来数年将持续改进此类方法，也期待与其他研究者合作优化代码与处理流程，以便所有人都能充分利用这一丰富的数据集。本数据处理流程并非NRCS的官方产品，任何研究中均不得将其表述为官方成果，仅为一种可用于SNOTEL网络数据的处理方案。

【代码使用方法】
本系列文件需按顺序依次运行。首先，运行DownloadSNOTEL.py文件，从目标站点与水文年下载逐小时与逐日SNOTEL数据。其次，运行SNOTEL_L0.py文件，移除午夜时间戳处的经逐日质量控制的数值。随后，依次运行SNOTEL_L1.py与SNOTEL_L2.py文件，自动完成1级与2级数据处理流程。

若希望手动清理数据（3级处理），可通过邮箱[e-mail]联系Anne Heggli获取相关流程。本人乐于分享并提供协助，但该流程较为复杂，若有使用者希望尝试，本人愿意逐一讲解相关方法。

【QA与QC标记说明】
标记 : 名称 : 说明
质量保证（QA）标记
R : 原始数据 : 未经过人工审核
F : 标记数据 : 已分配自动质量控制标记
P : 暂定数据 : 已完成初步人工审核
A : 已核准数据 : 已完成处理与最终审核
质量控制（QC）标记
V : 有效数据 : 观测值有效
E : 已编辑数据 : 已修改现有数值
S : 可疑数据 : 数值存疑

搜集汇总

背景与挑战

背景概述

Hourly SNOTEL Data是一个包含SNOTEL网络小时级气象数据的数据集，主要用于雪水当量和温度监测。数据集提供了数据下载和处理的Python脚本，并包含详细的质量控制信息，适用于气象和雪水研究。