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| Created: | May 22, 2023 at 4:50 a.m. | |
| Last updated: | Aug 15, 2024 at 12:09 a.m. | |
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| Content types: | Multidimensional Content |
| Sharing Status: | Public |
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Abstract
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.
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Coverage
Spatial
Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
Place/Area Name:
Baltimore, MD
North Latitude
39.6500°
East Longitude
-76.3000°
South Latitude
38.9500°
West Longitude
-77.0000°
Temporal
| Start Date: | |
|---|---|
| End Date: |
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Readme.txt
# Site Information # ---------------------------------- # SiteName: Baltimore, MD # Latitude: 38.95 to 39.65 # Longitude: -77.0 to -76.3 # LatLonDatum: NAD83 # State: Maryland # Counties: Baltimore, Baltimore City, Harford, Howard, Anne Arundel # Comments: # SiteType: Atmosphere # # Variable and Method Information # ---------------------------------- # VariableName: Precipitation # VariableUnitsName: millimeters of water # VariableUnitsType: depth # VariableUnitsAbbreviation: mm # # ValueType: Derived Value # DataType: Incremental # # GeneralCategory: Climate # SampleMedium: Precipitation # NoDataValue: # TimeSupport: 0.0 # TimeSupportUnitsAbbreviation: min # TimeSupportUnitsType: Time # TimeSupportUnitsName: minute # # MethodDescription: 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 below. 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. # Giangrande, S. E. and A.V. Ryzhkov. 2008. Estimation of rainfall based on the results of polarimetric echo classification. Journal of Applied Meteorology and Climatology, https://doi.org/10.1175/2008JAMC1753.1 # # Seo, B. -C., W. F. Krajewski, A. Kruger, P. Domaszczynski , J. A. Smith and M. Steiner. 2011. Radar-rainfall estimation algorithms of Hydro-NEXRAD, Journal of Hydroinformatics, https://doi.org/10.2166/hydro.2010.003 # # Smith, J. A., M. L.Baeck, G. Villarini, C. Welty, A. J. Miller, W. F. Krajewski. 2012. Analyses of a long-term high-resolution radar rainfall data set for the Baltimore metropolitan region, Water Resources Research, https://doi.org/10.1029/2011WR010641. # # Processing Level #----------------------------------- # QualityControlLevelCode: QC1 # QualityControlLevelDefinition: Quality controlled data. # QualityControlLevelExplanation: Data have passed quality control processing. # # Source Information # ---------------------------------- # Organization: Princeton University # ContactName: Mary Lynn Baeck # Email: mlbaeck@Princeton.EDU # Baeck, M. L., J. A. Smith (2023). Rainfall radar data for Baltimore, MD, USA, HydroShare, http://www.hydroshare.org/resource/ae004ca9deb442958c32f0457579c4f0 # # UTC Offset ----------------------------------- # UTCOffset: 0 # # Cell IDs ----------------------------------------- # GridCell Latitude Longitude 0 38.95 -77.0000 1 38.95 -76.9899 2 38.95 -76.9797 3 38.95 -76.9696 4 38.95 -76.9594 5 38.95 -76.9493 6 38.95 -76.9391 7 38.95 -76.9290 8 38.95 -76.9188 9 38.95 -76.9087 10 38.95 -76.8986 11 38.95 -76.8884 12 38.95 -76.8783 13 38.95 -76.8681 14 38.95 -76.8580 15 38.95 -76.8478 16 38.95 -76.8377 17 38.95 -76.8275 18 38.95 -76.8174 19 38.95 -76.8072 20 38.95 -76.7971 21 38.95 -76.7870 22 38.95 -76.7768 23 38.95 -76.7667 24 38.95 -76.7565 25 38.95 -76.7464 26 38.95 -76.7362 27 38.95 -76.7261 28 38.95 -76.7159 29 38.95 -76.7058 30 38.95 -76.6957 31 38.95 -76.6855 32 38.95 -76.6754 33 38.95 -76.6652 34 38.95 -76.6551 35 38.95 -76.6449 36 38.95 -76.6348 37 38.95 -76.6246 38 38.95 -76.6145 39 38.95 -76.6043 40 38.95 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1989 39.23 -76.7058 1990 39.23 -76.6957 1991 39.23 -76.6855 1992 39.23 -76.6754 1993 39.23 -76.6652 1994 39.23 -76.6551 1995 39.23 -76.6449 1996 39.23 -76.6348 1997 39.23 -76.6246 1998 39.23 -76.6145 1999 39.23 -76.6043 2000 39.23 -76.5942 2001 39.23 -76.5841 2002 39.23 -76.5739 2003 39.23 -76.5638 2004 39.23 -76.5536 2005 39.23 -76.5435 2006 39.23 -76.5333 2007 39.23 -76.5232 2008 39.23 -76.5130 2009 39.23 -76.5029 2010 39.23 -76.4928 2011 39.23 -76.4826 2012 39.23 -76.4725 2013 39.23 -76.4623 2014 39.23 -76.4522 2015 39.23 -76.4420 2016 39.23 -76.4319 2017 39.23 -76.4217 2018 39.23 -76.4116 2019 39.23 -76.4014 2020 39.23 -76.3913 2021 39.23 -76.3812 2022 39.23 -76.3710 2023 39.23 -76.3609 2024 39.23 -76.3507 2025 39.23 -76.3406 2026 39.23 -76.3304 2027 39.23 -76.3203 2028 39.23 -76.3101 2029 39.23 -76.3000 2030 39.24 -77.0000 2031 39.24 -76.9899 2032 39.24 -76.9797 2033 39.24 -76.9696 2034 39.24 -76.9594 2035 39.24 -76.9493 2036 39.24 -76.9391 2037 39.24 -76.9290 2038 39.24 -76.9188 2039 39.24 -76.9087 2040 39.24 -76.8986 2041 39.24 -76.8884 2042 39.24 -76.8783 2043 39.24 -76.8681 2044 39.24 -76.8580 2045 39.24 -76.8478 2046 39.24 -76.8377 2047 39.24 -76.8275 2048 39.24 -76.8174 2049 39.24 -76.8072 2050 39.24 -76.7971 2051 39.24 -76.7870 2052 39.24 -76.7768 2053 39.24 -76.7667 2054 39.24 -76.7565 2055 39.24 -76.7464 2056 39.24 -76.7362 2057 39.24 -76.7261 2058 39.24 -76.7159 2059 39.24 -76.7058 2060 39.24 -76.6957 2061 39.24 -76.6855 2062 39.24 -76.6754 2063 39.24 -76.6652 2064 39.24 -76.6551 2065 39.24 -76.6449 2066 39.24 -76.6348 2067 39.24 -76.6246 2068 39.24 -76.6145 2069 39.24 -76.6043 2070 39.24 -76.5942 2071 39.24 -76.5841 2072 39.24 -76.5739 2073 39.24 -76.5638 2074 39.24 -76.5536 2075 39.24 -76.5435 2076 39.24 -76.5333 2077 39.24 -76.5232 2078 39.24 -76.5130 2079 39.24 -76.5029 2080 39.24 -76.4928 2081 39.24 -76.4826 2082 39.24 -76.4725 2083 39.24 -76.4623 2084 39.24 -76.4522 2085 39.24 -76.4420 2086 39.24 -76.4319 2087 39.24 -76.4217 2088 39.24 -76.4116 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2239 39.26 -76.3000 2240 39.27 -77.0000 2241 39.27 -76.9899 2242 39.27 -76.9797 2243 39.27 -76.9696 2244 39.27 -76.9594 2245 39.27 -76.9493 2246 39.27 -76.9391 2247 39.27 -76.9290 2248 39.27 -76.9188 2249 39.27 -76.9087 2250 39.27 -76.8986 2251 39.27 -76.8884 2252 39.27 -76.8783 2253 39.27 -76.8681 2254 39.27 -76.8580 2255 39.27 -76.8478 2256 39.27 -76.8377 2257 39.27 -76.8275 2258 39.27 -76.8174 2259 39.27 -76.8072 2260 39.27 -76.7971 2261 39.27 -76.7870 2262 39.27 -76.7768 2263 39.27 -76.7667 2264 39.27 -76.7565 2265 39.27 -76.7464 2266 39.27 -76.7362 2267 39.27 -76.7261 2268 39.27 -76.7159 2269 39.27 -76.7058 2270 39.27 -76.6957 2271 39.27 -76.6855 2272 39.27 -76.6754 2273 39.27 -76.6652 2274 39.27 -76.6551 2275 39.27 -76.6449 2276 39.27 -76.6348 2277 39.27 -76.6246 2278 39.27 -76.6145 2279 39.27 -76.6043 2280 39.27 -76.5942 2281 39.27 -76.5841 2282 39.27 -76.5739 2283 39.27 -76.5638 2284 39.27 -76.5536 2285 39.27 -76.5435 2286 39.27 -76.5333 2287 39.27 -76.5232 2288 39.27 -76.5130 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4789 39.64 -76.7058 4790 39.64 -76.6957 4791 39.64 -76.6855 4792 39.64 -76.6754 4793 39.64 -76.6652 4794 39.64 -76.6551 4795 39.64 -76.6449 4796 39.64 -76.6348 4797 39.64 -76.6246 4798 39.64 -76.6145 4799 39.64 -76.6043 4800 39.64 -76.5942 4801 39.64 -76.5841 4802 39.64 -76.5739 4803 39.64 -76.5638 4804 39.64 -76.5536 4805 39.64 -76.5435 4806 39.64 -76.5333 4807 39.64 -76.5232 4808 39.64 -76.5130 4809 39.64 -76.5029 4810 39.64 -76.4928 4811 39.64 -76.4826 4812 39.64 -76.4725 4813 39.64 -76.4623 4814 39.64 -76.4522 4815 39.64 -76.4420 4816 39.64 -76.4319 4817 39.64 -76.4217 4818 39.64 -76.4116 4819 39.64 -76.4014 4820 39.64 -76.3913 4821 39.64 -76.3812 4822 39.64 -76.3710 4823 39.64 -76.3609 4824 39.64 -76.3507 4825 39.64 -76.3406 4826 39.64 -76.3304 4827 39.64 -76.3203 4828 39.64 -76.3101 4829 39.64 -76.3000 4830 39.65 -77.0000 4831 39.65 -76.9899 4832 39.65 -76.9797 4833 39.65 -76.9696 4834 39.65 -76.9594 4835 39.65 -76.9493 4836 39.65 -76.9391 4837 39.65 -76.9290 4838 39.65 -76.9188 4839 39.65 -76.9087 4840 39.65 -76.8986 4841 39.65 -76.8884 4842 39.65 -76.8783 4843 39.65 -76.8681 4844 39.65 -76.8580 4845 39.65 -76.8478 4846 39.65 -76.8377 4847 39.65 -76.8275 4848 39.65 -76.8174 4849 39.65 -76.8072 4850 39.65 -76.7971 4851 39.65 -76.7870 4852 39.65 -76.7768 4853 39.65 -76.7667 4854 39.65 -76.7565 4855 39.65 -76.7464 4856 39.65 -76.7362 4857 39.65 -76.7261 4858 39.65 -76.7159 4859 39.65 -76.7058 4860 39.65 -76.6957 4861 39.65 -76.6855 4862 39.65 -76.6754 4863 39.65 -76.6652 4864 39.65 -76.6551 4865 39.65 -76.6449 4866 39.65 -76.6348 4867 39.65 -76.6246 4868 39.65 -76.6145 4869 39.65 -76.6043 4870 39.65 -76.5942 4871 39.65 -76.5841 4872 39.65 -76.5739 4873 39.65 -76.5638 4874 39.65 -76.5536 4875 39.65 -76.5435 4876 39.65 -76.5333 4877 39.65 -76.5232 4878 39.65 -76.5130 4879 39.65 -76.5029 4880 39.65 -76.4928 4881 39.65 -76.4826 4882 39.65 -76.4725 4883 39.65 -76.4623 4884 39.65 -76.4522 4885 39.65 -76.4420 4886 39.65 -76.4319 4887 39.65 -76.4217 4888 39.65 -76.4116 4889 39.65 -76.4014 4890 39.65 -76.3913 4891 39.65 -76.3812 4892 39.65 -76.3710 4893 39.65 -76.3609 4894 39.65 -76.3507 4895 39.65 -76.3406 4896 39.65 -76.3304 4897 39.65 -76.3203 4898 39.65 -76.3101 4899 39.65 -76.3000
Data Services
The following web services are available for data contained in this resource. Geospatial Feature and Raster
data are made available via Open Geospatial Consortium Web Services. The provided links can be copied and pasted
into GIS software to access these data. Multidimensional NetCDF data are made available via a THREDDS Data Server
using remote data access protocols such as OPeNDAP. Other data services may be made available in the future to
support additional data types.
Related Resources
| Title | Owners | Sharing Status | My Permission |
|---|---|---|---|
| Dead Run Data Collection | Claire Welty · John Lagrosa IV | Discoverable & Shareable | Open Access |
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
|---|---|---|
| National Science Foundation | Quantifying Urban Groundwater in Environmental Field Observatories: A Missing Link in Understanding How the Built Environment Affects the Hydrologic Cycle | 0610009 |
| National Science Foundation | Engineering Research Center (ERC) on Mid-Infrared Technologies for Health and the Environment (MIRTHE) | 0540832 |
| National Oceanic and Atmospheric Administration | Coupled Patterns and Processes in Urban Landscapes | NA06OAR4310243 |
| National Science Foundation | Baltimore Ecosystem Study Phase III: Adaptive Processes in the Baltimore Socio-Ecological System from the Sanitary to the Sustainable City | 1027188 |
| National Science Foundation | Collaborative Research: Dynamic Coupling of the Water Cycle and Patterns of Urban Growth | 0709659 |
| National Oceanic and Atmospheric Administration | Integrating Real-Time Sensor Networks, Data Assimilation, and Predictive Modeling to Assess the Effects of Climate Variability on Water Resources in an Urbanizing Landscape | NA07OAR4170518 |
| National Science Foundation | Collaborative Research: ITR-(ASE+ECS)-(dms+sim): A Comprehensive Framework for Use of Next Generation Weather Radar (NEXRAD) Data in Hydrometeorology and Hydrology | 0427325 |
| National Oceanic and Atmospheric Administration | Integrating Climate Change into the Restoration of the Chesapeake Bay and Watershed | NA10OAR4310220 |
| National Science Foundation | Collaborative Research: ITR-(ASE+ECS)-(dms+sim): A Comprehensive Framework for Use of Next Generation Weather Radar (NEXRAD) Data in Hydrometeorology and Hydrology | 0427422 |
| National Science Foundation | LTER: Human Settlements as Ecosystems: Metropolitan Baltimore from 1797 - 2100: PHASE II | 0423476 |
| National Science Foundation | Collaborative Research, WSC-Category 2: Regional Climate Variability and Patterns of Urban Development - Impacts on the Urban Water Cycle and Nutrient Export | 1058027 |
| National Science Foundation | Collaborative Research: Network Cluster: Urban Critical Zone processes along the Piedmont-Coastal Plain transition | 2012340 |
Contributors
People or Organizations that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.
| Name | Organization | Address | Phone | Author Identifiers |
|---|---|---|---|---|
| Claire Welty | University of Maryland;Baltimore County | Maryland, US | ||
| John J. Lagrosa IV | Center for Urban Environmental Research and Education at the University of Maryland, Baltimore County | Maryland, US | ||
| Andrew Miller | UMBC | MD, US |
How to Cite
Baeck, M. L., J. A. Smith (2024). Radar rainfall data for Baltimore, MD, USA, HydroShare, http://www.hydroshare.org/resource/ae004ca9deb442958c32f0457579c4f0
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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