Radar rainfall data for Baltimore, MD, USA

The Baltimore radar rainfall dataset was developed from a multi-sensor analysis combining radar rainfall estimates from the Sterling, VA WSR88D radar (KLWX) with measurements from a collection of ground based rain gages. The archived data have a 15-minute time resolution and a grid resolution of 0.01 degree latitude/longitude (approximately 1 km x 1 km); 15-minute rainfall accumulations for each grid are in mm. The dataset spans 22 years, 2000-2021, and covers an area of approximately 4,900 km^2 (70 by 70 grids, each with approximate area of 1 km^2) surrounding the Baltimore, MD metropolitan area (Figure 1). The rainfall data cover the six months from April to September of each year. This is the period with most intense sub-daily rainfall and the period for which radar measurements are most accurate. Figure 1 illustrates the climatological analyses of mean annual frequency of days with at least 1 hour rainfall exceeding 25 mm. The striking spatial variability of convective rainfall is illustrated in Figure 2 by the April-September climatology of annual lightning strikes.

As with many long-term environmental data sets, sensor technology has changed during the time period of the archive. The Sterling, VA WSR88D radar underwent a hardware upgrade from single polarization to dual polarization in 2012. Prior to the upgrade, rainfall was estimated using a conventional radar-reflectivity algorithm (HydroNEXRAD) which converts reflectivity measurements in polar coordinates from the lowest sweep to rainfall estimates on a 0.01 degree latitude-longitude grid at the surface (see Seo et al. 2010 and Smith et al. 2012 for details on the algorithm). The polarimetric upgrade introduced new measurements into the radar-rainfall algorithm. In addition to reflectivity, the operational rainfall product, Digital Precipitation Rate (DPR), directly uses differential reflectivity and specific differential phase shift measurements to estimate rainfall (https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00708; see also Giangrande and Ryzhkov 2008). Details of the algorithm structure and parameterization for the DPR radar-rainfall estimates have been modified during the 10-year period of the data set.

A storm-based (daily) multiplicative mean field bias has been applied to both datasets. The mean field bias is computed as the ratio of daily rain gage rainfall at a point to daily radar rainfall for the bin that contains the gage. The rain gage dataset is compiled from rain gages in the Baltimore metropolitan region and surrounding areas and includes gages acquired from both Baltimore City and Baltimore County, and the Global Historical Climatology Network daily (GHCNd). Mean field bias improves rainfall estimates and diminishes the impacts of changing measurement procedures.

The dataset has been archived in 2 formats: netCDF gridded rainfall, 1 file for each 15-minute time period, and csv or excel format point rainfall (1 point at the center of each grid) in a timeseries format with 1 file per calendar month covering the entire 70x70 domain. The csv files are in folders organized by calendar year. The first five columns in each file represent year, month, day, hour, and minute and can be combined to generate a unique date-time value for each time step. Each additional column is a complete time series for the month and represents data from one of the 1-km2 grid cells in the original data set.

The latitude and longitude coordinates for each pixel in the grid are provided. The latitude and longitude represent the centroid of the cell, which is square when represented in latitude and longitude coordinates and rectangular when represented in other distance-based coordinate systems such as State Plane or Universal Transverse Mercator. There are 4900 pixels in the domain. In order to visualize the data using GIS or other software, the user needs to associate each column in the annual rainfall file with the latitude and longitude values for that grid cell number.

These data may be subject to modest revision or reformatting in future versions. The current version is version 2.0 and is being offered to users who wish to explore the data. We will revise this document as needed.

巴尔的摩雷达降雨数据集由多个传感器分析发展而来，该分析结合了弗吉尼亚州斯特林（Sterling, VA）WSR88D雷达（KLWX）的雷达降雨估计值以及一组地面雨量计的测量数据。该存档数据具有15分钟的时间分辨率，网格分辨率为0.01度经纬度（约1公里 x 1公里）；每个网格的15分钟降雨累积量以毫米为单位。数据集覆盖了22年，即2000年至2021年，并覆盖了约4900平方公里（70 x 70个网格，每个网格的面积约为1平方公里）的巴尔的摩，马里兰州大都市地区周边区域（见图1）。降雨数据覆盖了每年的4月至9月，这是日降雨强度最为剧烈的时期，也是雷达测量最为精确的时期。图1展示了至少1小时降雨量超过25毫米的日数平均年频率的气候分析。图2通过4月至9月的年雷击气候学，展示了对流降雨的显著空间变异性。

与许多长期环境数据集一样，在存档期间，传感器技术发生了变化。斯特林，VA的WSR88D雷达在2012年从单极化升级到双极化。在升级之前，降雨估计使用的是传统的雷达反射率算法（HydroNEXRAD），该算法将极坐标下最低扫描的反射率测量值转换为地面0.01度经纬度网格上的降雨估计值（详见Seo等人的2010年和Smith等人的2012年的算法细节）。极化升级引入了雷达降雨算法中的新测量值。除了反射率外，操作降雨产品数字降水率（DPR）直接使用差分反射率和特定差分相位偏移测量值来估计降雨（见https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00708；另见Giangrande和Ryzhkov的2008年的研究）。DPR雷达降雨估计的算法结构和参数化细节在数据集的10年期间进行了修改。

对两个数据集都应用了基于风暴（每日）的乘性平均场偏差。平均场偏差计算为某点的日雨量计降雨与包含该雨量计的格网日雷达降雨的比率。雨量计数据集由巴尔的摩大都市区域及其周边地区的雨量计组成，包括来自巴尔的摩市和巴尔的摩县的雨量计，以及全球历史气候学网络每日（GHCNd）的雨量计。平均场偏差改善了降雨估计，并减少了测量程序变化的影响。

该数据集以两种格式存档：netCDF网格降雨，每个15分钟时间段一个文件，以及csv或excel格式的点降雨（每个网格中心的一个点）时间序列格式，每个日历月一个文件，覆盖整个70x70区域。csv文件按日历年度组织在文件夹中。每个文件的前五列代表年、月、日、时和分，可以组合生成每个时间步的独特的日期时间值。每个附加列是该月的完整时间序列，代表原始数据集中1平方公里网格单元格的数据。

网格中每个像素的纬度和经度坐标均提供。纬度和经度代表单元格的重心，当以纬度和经度坐标表示时为正方形，当以其他基于距离的坐标系统（如州平面或通用横轴墨卡托）表示时为矩形。区域内有4900个像素。为了使用GIS或其他软件可视化数据，用户需要将年度降雨文件中的每个列与该网格单元格的纬度和经度值关联起来。

这些数据可能在未来的版本中经历适度的修订或重新格式化。当前版本为2.0版，并提供给希望探索数据的用户。我们将根据需要修订本文件。