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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 |
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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.
Subject Keywords
Coverage
Spatial
Temporal
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Content
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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1939 39.22 -76.5029 1940 39.22 -76.4928 1941 39.22 -76.4826 1942 39.22 -76.4725 1943 39.22 -76.4623 1944 39.22 -76.4522 1945 39.22 -76.4420 1946 39.22 -76.4319 1947 39.22 -76.4217 1948 39.22 -76.4116 1949 39.22 -76.4014 1950 39.22 -76.3913 1951 39.22 -76.3812 1952 39.22 -76.3710 1953 39.22 -76.3609 1954 39.22 -76.3507 1955 39.22 -76.3406 1956 39.22 -76.3304 1957 39.22 -76.3203 1958 39.22 -76.3101 1959 39.22 -76.3000 1960 39.23 -77.0000 1961 39.23 -76.9899 1962 39.23 -76.9797 1963 39.23 -76.9696 1964 39.23 -76.9594 1965 39.23 -76.9493 1966 39.23 -76.9391 1967 39.23 -76.9290 1968 39.23 -76.9188 1969 39.23 -76.9087 1970 39.23 -76.8986 1971 39.23 -76.8884 1972 39.23 -76.8783 1973 39.23 -76.8681 1974 39.23 -76.8580 1975 39.23 -76.8478 1976 39.23 -76.8377 1977 39.23 -76.8275 1978 39.23 -76.8174 1979 39.23 -76.8072 1980 39.23 -76.7971 1981 39.23 -76.7870 1982 39.23 -76.7768 1983 39.23 -76.7667 1984 39.23 -76.7565 1985 39.23 -76.7464 1986 39.23 -76.7362 1987 39.23 -76.7261 1988 39.23 -76.7159 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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 2089 39.24 -76.4014 2090 39.24 -76.3913 2091 39.24 -76.3812 2092 39.24 -76.3710 2093 39.24 -76.3609 2094 39.24 -76.3507 2095 39.24 -76.3406 2096 39.24 -76.3304 2097 39.24 -76.3203 2098 39.24 -76.3101 2099 39.24 -76.3000 2100 39.25 -77.0000 2101 39.25 -76.9899 2102 39.25 -76.9797 2103 39.25 -76.9696 2104 39.25 -76.9594 2105 39.25 -76.9493 2106 39.25 -76.9391 2107 39.25 -76.9290 2108 39.25 -76.9188 2109 39.25 -76.9087 2110 39.25 -76.8986 2111 39.25 -76.8884 2112 39.25 -76.8783 2113 39.25 -76.8681 2114 39.25 -76.8580 2115 39.25 -76.8478 2116 39.25 -76.8377 2117 39.25 -76.8275 2118 39.25 -76.8174 2119 39.25 -76.8072 2120 39.25 -76.7971 2121 39.25 -76.7870 2122 39.25 -76.7768 2123 39.25 -76.7667 2124 39.25 -76.7565 2125 39.25 -76.7464 2126 39.25 -76.7362 2127 39.25 -76.7261 2128 39.25 -76.7159 2129 39.25 -76.7058 2130 39.25 -76.6957 2131 39.25 -76.6855 2132 39.25 -76.6754 2133 39.25 -76.6652 2134 39.25 -76.6551 2135 39.25 -76.6449 2136 39.25 -76.6348 2137 39.25 -76.6246 2138 39.25 -76.6145 2139 39.25 -76.6043 2140 39.25 -76.5942 2141 39.25 -76.5841 2142 39.25 -76.5739 2143 39.25 -76.5638 2144 39.25 -76.5536 2145 39.25 -76.5435 2146 39.25 -76.5333 2147 39.25 -76.5232 2148 39.25 -76.5130 2149 39.25 -76.5029 2150 39.25 -76.4928 2151 39.25 -76.4826 2152 39.25 -76.4725 2153 39.25 -76.4623 2154 39.25 -76.4522 2155 39.25 -76.4420 2156 39.25 -76.4319 2157 39.25 -76.4217 2158 39.25 -76.4116 2159 39.25 -76.4014 2160 39.25 -76.3913 2161 39.25 -76.3812 2162 39.25 -76.3710 2163 39.25 -76.3609 2164 39.25 -76.3507 2165 39.25 -76.3406 2166 39.25 -76.3304 2167 39.25 -76.3203 2168 39.25 -76.3101 2169 39.25 -76.3000 2170 39.26 -77.0000 2171 39.26 -76.9899 2172 39.26 -76.9797 2173 39.26 -76.9696 2174 39.26 -76.9594 2175 39.26 -76.9493 2176 39.26 -76.9391 2177 39.26 -76.9290 2178 39.26 -76.9188 2179 39.26 -76.9087 2180 39.26 -76.8986 2181 39.26 -76.8884 2182 39.26 -76.8783 2183 39.26 -76.8681 2184 39.26 -76.8580 2185 39.26 -76.8478 2186 39.26 -76.8377 2187 39.26 -76.8275 2188 39.26 -76.8174 2189 39.26 -76.8072 2190 39.26 -76.7971 2191 39.26 -76.7870 2192 39.26 -76.7768 2193 39.26 -76.7667 2194 39.26 -76.7565 2195 39.26 -76.7464 2196 39.26 -76.7362 2197 39.26 -76.7261 2198 39.26 -76.7159 2199 39.26 -76.7058 2200 39.26 -76.6957 2201 39.26 -76.6855 2202 39.26 -76.6754 2203 39.26 -76.6652 2204 39.26 -76.6551 2205 39.26 -76.6449 2206 39.26 -76.6348 2207 39.26 -76.6246 2208 39.26 -76.6145 2209 39.26 -76.6043 2210 39.26 -76.5942 2211 39.26 -76.5841 2212 39.26 -76.5739 2213 39.26 -76.5638 2214 39.26 -76.5536 2215 39.26 -76.5435 2216 39.26 -76.5333 2217 39.26 -76.5232 2218 39.26 -76.5130 2219 39.26 -76.5029 2220 39.26 -76.4928 2221 39.26 -76.4826 2222 39.26 -76.4725 2223 39.26 -76.4623 2224 39.26 -76.4522 2225 39.26 -76.4420 2226 39.26 -76.4319 2227 39.26 -76.4217 2228 39.26 -76.4116 2229 39.26 -76.4014 2230 39.26 -76.3913 2231 39.26 -76.3812 2232 39.26 -76.3710 2233 39.26 -76.3609 2234 39.26 -76.3507 2235 39.26 -76.3406 2236 39.26 -76.3304 2237 39.26 -76.3203 2238 39.26 -76.3101 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 2289 39.27 -76.5029 2290 39.27 -76.4928 2291 39.27 -76.4826 2292 39.27 -76.4725 2293 39.27 -76.4623 2294 39.27 -76.4522 2295 39.27 -76.4420 2296 39.27 -76.4319 2297 39.27 -76.4217 2298 39.27 -76.4116 2299 39.27 -76.4014 2300 39.27 -76.3913 2301 39.27 -76.3812 2302 39.27 -76.3710 2303 39.27 -76.3609 2304 39.27 -76.3507 2305 39.27 -76.3406 2306 39.27 -76.3304 2307 39.27 -76.3203 2308 39.27 -76.3101 2309 39.27 -76.3000 2310 39.28 -77.0000 2311 39.28 -76.9899 2312 39.28 -76.9797 2313 39.28 -76.9696 2314 39.28 -76.9594 2315 39.28 -76.9493 2316 39.28 -76.9391 2317 39.28 -76.9290 2318 39.28 -76.9188 2319 39.28 -76.9087 2320 39.28 -76.8986 2321 39.28 -76.8884 2322 39.28 -76.8783 2323 39.28 -76.8681 2324 39.28 -76.8580 2325 39.28 -76.8478 2326 39.28 -76.8377 2327 39.28 -76.8275 2328 39.28 -76.8174 2329 39.28 -76.8072 2330 39.28 -76.7971 2331 39.28 -76.7870 2332 39.28 -76.7768 2333 39.28 -76.7667 2334 39.28 -76.7565 2335 39.28 -76.7464 2336 39.28 -76.7362 2337 39.28 -76.7261 2338 39.28 -76.7159 2339 39.28 -76.7058 2340 39.28 -76.6957 2341 39.28 -76.6855 2342 39.28 -76.6754 2343 39.28 -76.6652 2344 39.28 -76.6551 2345 39.28 -76.6449 2346 39.28 -76.6348 2347 39.28 -76.6246 2348 39.28 -76.6145 2349 39.28 -76.6043 2350 39.28 -76.5942 2351 39.28 -76.5841 2352 39.28 -76.5739 2353 39.28 -76.5638 2354 39.28 -76.5536 2355 39.28 -76.5435 2356 39.28 -76.5333 2357 39.28 -76.5232 2358 39.28 -76.5130 2359 39.28 -76.5029 2360 39.28 -76.4928 2361 39.28 -76.4826 2362 39.28 -76.4725 2363 39.28 -76.4623 2364 39.28 -76.4522 2365 39.28 -76.4420 2366 39.28 -76.4319 2367 39.28 -76.4217 2368 39.28 -76.4116 2369 39.28 -76.4014 2370 39.28 -76.3913 2371 39.28 -76.3812 2372 39.28 -76.3710 2373 39.28 -76.3609 2374 39.28 -76.3507 2375 39.28 -76.3406 2376 39.28 -76.3304 2377 39.28 -76.3203 2378 39.28 -76.3101 2379 39.28 -76.3000 2380 39.29 -77.0000 2381 39.29 -76.9899 2382 39.29 -76.9797 2383 39.29 -76.9696 2384 39.29 -76.9594 2385 39.29 -76.9493 2386 39.29 -76.9391 2387 39.29 -76.9290 2388 39.29 -76.9188 2389 39.29 -76.9087 2390 39.29 -76.8986 2391 39.29 -76.8884 2392 39.29 -76.8783 2393 39.29 -76.8681 2394 39.29 -76.8580 2395 39.29 -76.8478 2396 39.29 -76.8377 2397 39.29 -76.8275 2398 39.29 -76.8174 2399 39.29 -76.8072 2400 39.29 -76.7971 2401 39.29 -76.7870 2402 39.29 -76.7768 2403 39.29 -76.7667 2404 39.29 -76.7565 2405 39.29 -76.7464 2406 39.29 -76.7362 2407 39.29 -76.7261 2408 39.29 -76.7159 2409 39.29 -76.7058 2410 39.29 -76.6957 2411 39.29 -76.6855 2412 39.29 -76.6754 2413 39.29 -76.6652 2414 39.29 -76.6551 2415 39.29 -76.6449 2416 39.29 -76.6348 2417 39.29 -76.6246 2418 39.29 -76.6145 2419 39.29 -76.6043 2420 39.29 -76.5942 2421 39.29 -76.5841 2422 39.29 -76.5739 2423 39.29 -76.5638 2424 39.29 -76.5536 2425 39.29 -76.5435 2426 39.29 -76.5333 2427 39.29 -76.5232 2428 39.29 -76.5130 2429 39.29 -76.5029 2430 39.29 -76.4928 2431 39.29 -76.4826 2432 39.29 -76.4725 2433 39.29 -76.4623 2434 39.29 -76.4522 2435 39.29 -76.4420 2436 39.29 -76.4319 2437 39.29 -76.4217 2438 39.29 -76.4116 2439 39.29 -76.4014 2440 39.29 -76.3913 2441 39.29 -76.3812 2442 39.29 -76.3710 2443 39.29 -76.3609 2444 39.29 -76.3507 2445 39.29 -76.3406 2446 39.29 -76.3304 2447 39.29 -76.3203 2448 39.29 -76.3101 2449 39.29 -76.3000 2450 39.31 -77.0000 2451 39.31 -76.9899 2452 39.31 -76.9797 2453 39.31 -76.9696 2454 39.31 -76.9594 2455 39.31 -76.9493 2456 39.31 -76.9391 2457 39.31 -76.9290 2458 39.31 -76.9188 2459 39.31 -76.9087 2460 39.31 -76.8986 2461 39.31 -76.8884 2462 39.31 -76.8783 2463 39.31 -76.8681 2464 39.31 -76.8580 2465 39.31 -76.8478 2466 39.31 -76.8377 2467 39.31 -76.8275 2468 39.31 -76.8174 2469 39.31 -76.8072 2470 39.31 -76.7971 2471 39.31 -76.7870 2472 39.31 -76.7768 2473 39.31 -76.7667 2474 39.31 -76.7565 2475 39.31 -76.7464 2476 39.31 -76.7362 2477 39.31 -76.7261 2478 39.31 -76.7159 2479 39.31 -76.7058 2480 39.31 -76.6957 2481 39.31 -76.6855 2482 39.31 -76.6754 2483 39.31 -76.6652 2484 39.31 -76.6551 2485 39.31 -76.6449 2486 39.31 -76.6348 2487 39.31 -76.6246 2488 39.31 -76.6145 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2539 39.32 -76.8072 2540 39.32 -76.7971 2541 39.32 -76.7870 2542 39.32 -76.7768 2543 39.32 -76.7667 2544 39.32 -76.7565 2545 39.32 -76.7464 2546 39.32 -76.7362 2547 39.32 -76.7261 2548 39.32 -76.7159 2549 39.32 -76.7058 2550 39.32 -76.6957 2551 39.32 -76.6855 2552 39.32 -76.6754 2553 39.32 -76.6652 2554 39.32 -76.6551 2555 39.32 -76.6449 2556 39.32 -76.6348 2557 39.32 -76.6246 2558 39.32 -76.6145 2559 39.32 -76.6043 2560 39.32 -76.5942 2561 39.32 -76.5841 2562 39.32 -76.5739 2563 39.32 -76.5638 2564 39.32 -76.5536 2565 39.32 -76.5435 2566 39.32 -76.5333 2567 39.32 -76.5232 2568 39.32 -76.5130 2569 39.32 -76.5029 2570 39.32 -76.4928 2571 39.32 -76.4826 2572 39.32 -76.4725 2573 39.32 -76.4623 2574 39.32 -76.4522 2575 39.32 -76.4420 2576 39.32 -76.4319 2577 39.32 -76.4217 2578 39.32 -76.4116 2579 39.32 -76.4014 2580 39.32 -76.3913 2581 39.32 -76.3812 2582 39.32 -76.3710 2583 39.32 -76.3609 2584 39.32 -76.3507 2585 39.32 -76.3406 2586 39.32 -76.3304 2587 39.32 -76.3203 2588 39.32 -76.3101 2589 39.32 -76.3000 2590 39.33 -77.0000 2591 39.33 -76.9899 2592 39.33 -76.9797 2593 39.33 -76.9696 2594 39.33 -76.9594 2595 39.33 -76.9493 2596 39.33 -76.9391 2597 39.33 -76.9290 2598 39.33 -76.9188 2599 39.33 -76.9087 2600 39.33 -76.8986 2601 39.33 -76.8884 2602 39.33 -76.8783 2603 39.33 -76.8681 2604 39.33 -76.8580 2605 39.33 -76.8478 2606 39.33 -76.8377 2607 39.33 -76.8275 2608 39.33 -76.8174 2609 39.33 -76.8072 2610 39.33 -76.7971 2611 39.33 -76.7870 2612 39.33 -76.7768 2613 39.33 -76.7667 2614 39.33 -76.7565 2615 39.33 -76.7464 2616 39.33 -76.7362 2617 39.33 -76.7261 2618 39.33 -76.7159 2619 39.33 -76.7058 2620 39.33 -76.6957 2621 39.33 -76.6855 2622 39.33 -76.6754 2623 39.33 -76.6652 2624 39.33 -76.6551 2625 39.33 -76.6449 2626 39.33 -76.6348 2627 39.33 -76.6246 2628 39.33 -76.6145 2629 39.33 -76.6043 2630 39.33 -76.5942 2631 39.33 -76.5841 2632 39.33 -76.5739 2633 39.33 -76.5638 2634 39.33 -76.5536 2635 39.33 -76.5435 2636 39.33 -76.5333 2637 39.33 -76.5232 2638 39.33 -76.5130 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Data Services
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
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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