% precip_categories_sky_conditions.readme.txt % % Fri Mar 22 09:28:03 MDT 2019 % SpB. (sean.burns@colorado.edu) This is a README file for a dataset which describes the precipitation state at the Niwot Ridge AmeriFlux tower (US-NR1). The header of each data file includes additional information and is repeated here: % % % NOTES: % % * These precip_categories_sky_conditions_* data files contain information % about the precipitation status for each given day. For additional details, % please see the following paper: % % Burns, S.P., P.D. Blanken, A.A. Turnipseed, J. Hu, and R.K. Monson, 2015: % The influence of warm-season precipitation on the diel cycle of the surface % energy balance and carbon dioxide at a Colorado subalpine forest site. % Biogeosciences, 12, 7349–7377, doi:10.5194/bg-12-7349-2015 % % * The precipitation status or state (s_wet_dry_spb) is defined as follows: % % dDry-Clear = -1 (Dry day, preceded by a Dry day, w/ clear skies) % dDry-Cloudy = 1 (Dry day, preceded by a Dry day, w/ cloudy skies) % wDry = 2 (Dry day, preceded by a Wet day) % dWet = 3 (Wet day, preceded by a Dry day) % wWet = 4 (Wet day, preceded by a Wet day) % % * A wet day is defined as a day (0-24 MST) when the cumulative rain total is % greater than 3mm % % * In the 2015 paper, a dDry-Clear day was determined by scaling the top-of- % the-atmosphere radiation by 0.8, and then comparing it to the Rsw_in at % the top of the tower. When the measured radiation was at least 2 percent % of scaled toa radiation, it was deemed a dDry-Clear day. % % * It should be noted that wDry days might also contain clear-sky days (this % distinction is not made here). % % * It should also be noted that the 2015 paper focused on the warm-season, % one should expect different cold-season results, when snow is present. % % * Missing data are set to NaN % % * For other specific details please see: % % http://urquell.colorado.edu/calendar/ % % http://urquell.colorado.edu/data_ameriflux/docs/ % % % -------- % % Columns are: % % 1-6. Year, Month, Day, Hour, Minute, Sec -- in MST, Time Stamp Corresponds to center of Averaging Time Period % 07. Decimal Day of Year (MST) % 08. Rsw_in_TOA W/m2 TOA Top-of-Atmosphere Incoming Shortwave Radiation Nautical Almanac % 09. Rsw_in_25m_KZ W/m2 25.5m Incoming Shortwave Radiation Kipp and Zonen CNR1 % 10. precip_mm mm 10.5m Cumulative Daily Precipitation Met One Model 385 or USCRN Precip Data % 11. wet_dry_spb mm NA Precipitation State for day NA % -------- % The following paper also used this same method to catergorize the data: Burns, S.P., S.C. Swenson, W.R. Wieder, D.M. Lawrence, G.B. Bonan, J.F. Knowles, and P.D. Blanken, 2018: A comparison of the diel cycle of modeled and measured latent heat flux during the warm season in a Colorado subalpine forest. Journal of Advances in Modeling Earth Systems (JAMES), 10, 617-651, doi:10.1002/2017MS001248 The data files with the precipitation type are all contained in a zip file which is: http://urquell.colorado.edu/data_ameriflux/data_supplemental/precip_categories_sky_conditions.zip the files within the zip file are: -rw-r--r-- 1 sburns staff 227429 Mar 21 10:03 precip_categories_sky_conditions_1998_ver.2019.03.21.dat -rw-r--r-- 1 sburns staff 1336417 Mar 21 10:03 precip_categories_sky_conditions_1999_ver.2019.03.21.dat -rw-r--r-- 1 sburns staff 1340066 Mar 21 10:03 precip_categories_sky_conditions_2000_ver.2019.03.21.dat -rw-r--r-- 1 sburns staff 1336417 Mar 21 10:03 precip_categories_sky_conditions_2001_ver.2019.03.21.dat -rw-r--r-- 1 sburns staff 1336417 Mar 21 10:04 precip_categories_sky_conditions_2002_ver.2019.03.21.dat -rw-r--r-- 1 sburns staff 1336417 Mar 21 10:04 precip_categories_sky_conditions_2003_ver.2019.03.21.dat -rw-r--r-- 1 sburns staff 1340065 Mar 21 10:04 precip_categories_sky_conditions_2004_ver.2019.03.21.dat -rw-r--r-- 1 sburns staff 1336417 Mar 21 10:04 precip_categories_sky_conditions_2005_ver.2019.03.21.dat -rw-r--r-- 1 sburns staff 1336417 Mar 21 10:04 precip_categories_sky_conditions_2006_ver.2019.03.21.dat -rw-r--r-- 1 sburns staff 1336417 Mar 21 10:05 precip_categories_sky_conditions_2007_ver.2019.03.21.dat -rw-r--r-- 1 sburns staff 1340065 Mar 21 10:05 precip_categories_sky_conditions_2008_ver.2019.03.21.dat -rw-r--r-- 1 sburns staff 1336417 Mar 21 10:05 precip_categories_sky_conditions_2009_ver.2019.03.21.dat -rw-r--r-- 1 sburns staff 1336417 Mar 21 10:05 precip_categories_sky_conditions_2010_ver.2019.03.21.dat -rw-r--r-- 1 sburns staff 1336417 Mar 21 10:05 precip_categories_sky_conditions_2011_ver.2019.03.21.dat -rw-r--r-- 1 sburns staff 1340065 Mar 21 10:06 precip_categories_sky_conditions_2012_ver.2019.03.21.dat -rw-r--r-- 1 sburns staff 1336417 Mar 21 10:06 precip_categories_sky_conditions_2013_ver.2019.03.21.dat -rw-r--r-- 1 sburns staff 1336417 Mar 21 10:06 precip_categories_sky_conditions_2014_ver.2019.03.21.dat -rw-r--r-- 1 sburns staff 1336417 Mar 21 10:06 precip_categories_sky_conditions_2015_ver.2019.03.21.dat -rw-r--r-- 1 sburns staff 1340065 Mar 21 10:07 precip_categories_sky_conditions_2016_ver.2019.03.21.dat -rw-r--r-- 1 sburns staff 1336417 Mar 21 10:07 precip_categories_sky_conditions_2017_ver.2019.03.21.dat -rw-r--r-- 1 sburns staff 1336417 Mar 21 10:07 precip_categories_sky_conditions_2018_ver.2019.03.21.dat As an example of these data, a simple plot (in MATLAB) is: load precip_categories_sky_conditions_2017_ver.2019.03.21.dat data=precip_categories_sky_conditions_2017_ver_2019_03_21; figure; plot(data(:,7),data(:,8),'-',data(:,7),data(:,9),data(:,7),100.*data(:,11),'.',data(:,7),10.*data(:,10),'.') set(gca,'xlim',[152 160]); grid on; legend('Top-of-Atmos Radiation','Rsw_in','100.*precip_status','100.*cumulative_daily_precip') xlabel('Day of Year 2017 [MST]') Here, you can see that day 155 was determined to be dDry-Clear and there was enough precip on day 157 to make this a dWet day...day 158 was then a wDry day... If one wants to plot all the dDry-Clear days for a given year, this can be done with: r=find(data(:,11)==-1); plot(data(r,7),data(r,8),'-',data(r,7),data(r,9))