This AUTHOR_readme.txt file was generated on 20200516 by Leah Campbell Links to Publication Field updated. 2021-12-09, SES. ------------------- GENERAL INFORMATION ------------------- 1. Title of Dataset Chilean Orographic and Mesoscale Precipitation Study (ChOMPS) Data 2. Author Information Principal Investigator Contact Information Name: Leah Campbell Institution: University of Utah Address: Department of Atmospheric Sciences WBB, 135 S 1460 E, Room 819 Salt Lake City, UT 84112 Email: leah.campbell@utah.edu Associate or Co-investigator Contact Information Name: Rene Garreaud Institution: Universidad de Chile Address: Departamento de Geofisica Blanco Encalada 2002 Santiago, Chile Email: rgarreau@dgf.uchile.cl Alternate Contact Information Name: Justin Minder Institution: University at Albany Address: Department of Atmospheric and Environmental Sciences ES 339B, 1400 Washington Avenue Albany, NY 12222 Email: jminder@albany.edu 3. Date of data collection (single date, range, approximate date) 20160515 - 20161007 4. Geographic location of data collection (where was data collected?): Concepcion Site: -36.8279 lat, -73.0347 long Chillan Site: -36.595 lat, -73.0799 long Las Trancas Site: 36.909 lat, -71.484 long 5. Information about funding sources that supported the collection of the data: This project was funded by grants from three entities: - Fulbright U.S. Student Program - University of Utah Global Change and Sustainability Center - University of Utah Mountain Meteorology Fund -------------------------- SHARING/ACCESS INFORMATION -------------------------- 1. Licenses/restrictions placed on the data: Available for use non-commercially and with attribution. 2. Links to publications that cite or use the data: Rojas, Y.; Minder, J.R.; Campbell, L.S.; Massmann, A.; Garreaud, R. Assessment of GPM IMERG satellite precipitation estimation and its dependence on microphysical rain regimes over the mountains of south-central Chile. Atmos. Res. 2021, 253,105454. https://doi.org/10.1016/j.atmosres.2021.105454 3. Links to other publicly accessible locations of the data: N/A 4. Links/relationships to ancillary data sets: N/A 5. Was data derived from another source? No 6. Recommended citation for the data: Campbell, Leah S., Rene Garreaud, and Justin Minder, 2016. The Chilean Orographic and Mesoscale Precipitation Study (ChOMPS) Data. The Hive: University of Utah Research Data Repository. --------------------- DATA & FILE OVERVIEW --------------------- 1. File List A. Filename: meteorological_station_data_lastrancas.tar.gz Short description: Contains meteorological station data from the Las Trancas site. Includes a readme file with a detailed site description and data information. B. Filename: parsivel_disdrometer_data.tar.gz Short description: Contains Parsivel disdrometer data from two sites: Chillan and Las Trancas. Folders for each site includes a readme file with a detailed site description and data information. C. Filename: mrr_data_raw_chillan.tar.gz Short description: Contains raw (unprocessed) Micro-Rain-Radar (MRR) data from the Chillan site. Includes a readme file with a detailed site description and data information. D. Filename: mrr_data_raw_concepcion.tar.gz Short description: Contains raw (unprocessed) Micro-Rain-Radar (MRR) data from the Concepcion site. Includes a readme file with a detailed site description and data information. E. Filename: mrr_data_raw_lastrancas.tar.gz Short description: Contains raw (unprocessed) Micro-Rain-Radar (MRR) data from the Las Trancas site. Includes a readme file with a detailed site description and data information. F. Filename: mrr_data_processed_chillan.tar.gz Short description: Contains processed Micro-Rain-Radar (MRR) data from the Chillan site. Includes a readme file with a detailed site description, data information, and processing methods. G. Filename: mrr_data_processed_concepcion.tar.gz Short description: Contains processed Micro-Rain-Radar (MRR) data from the Concepcion site. Includes a readme file with a detailed site description, data information, and processing methods. H. Filename: mrr_data_processed_lastrancas.tar.gz Short description: Contains processed Micro-Rain-Radar (MRR) data from the Las Trancas site. Includes a readme file with a detailed site description, data information, and processing methods. 2. Relationship between files: Each file contains data collected from a single instrument type during the ChOMPS field campaign. The meteorological station data and Parsivel disdrometer data files include data from all sites. The MRR files, are broken down by site and include both raw and processed data in separate files. 3. Additional related data collected that was not included in the current data package: N/A 4. Are there multiple versions of the dataset? No -------------------------- METHODOLOGICAL INFORMATION -------------------------- 1. Description of methods used for collection/generation of data: Meteorological Station Data: This site, located at Las Trancas, consisted of a tripod that held an anemometer, a temperature and relative humidity sensor, and a datalogger box which contained the barometer. A precipitation gauge was located about 2 m away from the tripod. a) Anemometer: RM Young 05103 The anemometer was mounted on top of the tripod at a height of approximately 3 m above the ground. The purpose of mounting the anemometer in this location was primarily to provide information about wind conditions that might affect the precipitation gauge measurements. b) Temperature and relative humidity probe: CS215 The temperature and relative humidity probe was mounted in a radiation shield on the north side of the tripod at approximately 1.5 m above the ground. c) Weighing precipitation gauge: ETI NOAH II The precipitation gauge was mounted on a levelable board on top of a 0.5-meter tall tower of cement cinder blocks. The gauge was surrounded by an Alter shield to reduce undercatch. The precipitation gauge was filled with a few inches of an antifreeze/hydraulic oil mixture, then was drained and refilled periodically. The body of the precipitation gauge was wrapped in heat tape to keep the liquid in the chamber from freezing at very cold temperatures and/or during high snowfall rates. d) Pressure Sensor: CS106 Barometric Pressure Sensor The barometer was mounted inside a weather-proof datalogger box on the tripod, at a height of about 1 meter above the ground. Disdrometer Data: The OTT Parsivel2 Present Weather Sensor transmits a laser beam between a transmitter and receiver head. As precipitation particles fall through the beam, they block off a portion of the beam, reducing the output voltage, and this is used to determine the particle diameter. The time it takes to pass through the beam is used to calculate the fall velocity of the particle. Using theoretical fall speed / particle size relations, the Parsivel produces a precipitation type. Micro-Rain-Radar Data: All three radars are the Metek ‘MRR-2’ model. The MRR-2 operates at a frequency of 24.230 GHz, with modulation of 0.5 to 15GHz according to the height resolution. The instruments sample 31 range gates and allow the user set the distance between the range gates from 10–1000 m. For the ChOMPS project, all 3 MRRs were programmed to switch between 50 m and 250 m range gates approximately every minute. The MRR records raw Doppler spectra at one range gate at a time, averaged over 10 s, which is output in 10 s intervals in the “.raw” data. Only one file is created per day (dates and times in files and file names are UTC), so it is constantly appended with each new output, and contains both 50 m and 250 m output. METEK GmbH, MRR Physical Basics, Version of 13 March 2012, Elmshorn, 20 pp., 2012 2. Methods for processing the data: Meteorological Station Data: None. Disdrometer Data: None. Micro-Rain-Radar Data: We chose to use the Maahn and Kollias (2012; hereafter MK) MRR data processing algorithm rather than using the default Metek processing method. The Metek processing method is intended for rain only, and results in a number of deficiencies in the dataset when there is snow observed either at the ground or above the melting level, which is the case for much of this dataset. While the MK algorithm is intended for snow events, we find that it works better over all for mixed snow/rain events than the Metek rain algorithm. MK warn that the algorithm may not work well in heavy rain and or strong turbulence, however this is also an issue in the Metek algorithm, and in the case of heavy rain is a result of the wavelength of the radar and resultant attenuation issues. Additionally, the MK noise removal and quality control algorithm is more rigorous than the Metek algorithm, and results in a cleaner dataset for analysis. Maahn, M. and P. Kollias, 2012: Improved Micro Rain Radar snow measurements using Doppler spectra post-processing. Atmos. Meas. Tech., 5, 2661–2673. 3. Instrument- or software-specific information needed to interpret the data: Meteorological Station Data: Sensor specifications: a) Anemometer: RM Young 05103 Wind speed: 0-224 mph Wind direction: 0 to 360 degrees Operating temperature: -50C to +50C b) Temperature and relative humidity probe: CS215 Temp range: -40C to +70C Relative humidity 0% to 100% Accuracy: +/- 0.3C at 25C c) Weighing precipitation gauge: ETI NOAH II Capacity: 12 inch Accuracy: +/- 0.01 in Operating temperature: -30C to +50C d) Pressure Sensor: CS106 Barometric Pressure Sensor Accuracy: +/- 0.3 mb at +20C, +/- 0.6 mb at 0 to +40C Operating temperature: -40C to +60C Disdrometer Data: OTT Parsivel Disdrometer. Please see manual for detailed information. Sensor specifications: Size range for liquid particles: 0.2 – 5 mm Size range for solid particles: 0.2 – 25 mm Velocity range: 0.2 – 20 m s-1 Micro-Rain-Radar Data: See References Above. 4. Standards and calibration information, if appropriate: Meteorological Station Data: N/A Disdrometer Data: N/A Micro-Rain-Radar Data: N/A 5. Environmental/experimental conditions: Meteorological Station Data: Station was located at Las Trancas, which was frequently stormy and had an unreliable power grid. However the station had a backup battery that enabled the station to collect data for the entire measurement period. There was no missing data for the extent of the field program. Disdrometer Data: One of the interface converters that came with the Parsivels was faulty, resulting in timing errors. Until the problem was figured out and a new converter was installed on July 11, there was only one Parsivel in operation at a time. After that period the Parsivels were quite reliable and data is only not available for periods where there was a power outage. 1) Universidad de Concepción, Chillán, Chile (UALB1): Data collection period: May 17, 2016 to October 07, 2016 Missing data (YYYYMMDD HHMM): 20160805 0004 to 20160811 1303 UTC 20161004 1809 to 20161006 1134 UTC Spotty data (faulty converter): June 3, 1841 UTC to July 11, 1939 UTC 2) Valle Las Trancas, Chile (UALB2): Data collection period: June 12, 2016 to October 07, 2016 Missing data (YYYYMMDD HHMM): 20160614 1430 to 1438 UTC 20160615 2101 to 2359 UTC 20160620 2101 to 20160621 2359 UTC 20160629 0914 to 2052 UTC 20160713 1902 to 0751 UTC 20160716 0134 to 1302 UTC 20160717 0206 to 0219 UTC 20160717 2138 to 2239 UTC 20160814 2101 to 20160815 0221 UTC 20160912 1902 to 20160916 2245 UTC Micro-Rain-Radar Data: 1) Universidad de Concepcion, Concepcion, Chile: a)A band of interference appeared on July 1st and persisted through the end of the field campaign, typically affecting 1-3 range gates at a constant height that would remain constant for several weeks before periodically changing height. Due to concerns about how this interference might affect data quality within those range gates, we chose to remove those range gates entirely at the noise removal stage, prior to dealiasing and calculation of reflectivity parameters. After the conclusion of the field study we tested the radar again at a different site in Santiago and no interference was observed. b) Missing data periods (mmdd: HHMM-HHMM UTC): 0529: 1732-1759 UTC (unexpected computer shutdown) 0622: 1736-2048 UTC 0630: 1805 UTC 0707: 2007-2009 UTC 0731: 2306-2312 UTC 0801: 1448-1602, 1906-1914 UTC (move to new location) 2) Universidad de Concepción, Chillán, Chile: a) Weather conditions were generally more mild at this location compared to the other two and the power source was more consistent. b) Missing data periods (mmdd: HHMM-HHMM UTC): 0615: 1912-2054 UTC 0805: 0004-0005 UTC 1004: 1807-1832 UTC 3) Valle Las Trancas, Chile: a) Several days of raw data are completely missing from the dataset in Las Trancas (05/23, 05/24, and 05/30). When the MRR was first installed, it ran on the Minderfield3 computer first (05/14-05/22), before being switched to the dedicated mrr1 computer. We do not believe that this made any difference in the dataset. Power outages occurred relatively frequently in Las Trancas, especially during heavy precipitation and/or holiday weekends when the power grid had noticeable issues. b) Missing data periods (mmdd: HHMM-HHMM UTC): 0515: 1858-2042 UTC 0516: 2239-2241 UTC 0518: 1426 UTC 0519: 1931-1939 UTC 0520: 0918 UTC 0522: 1910-2359 UTC 0523 – all day 0524 – all day 0526 – all day 0527: 0000-2118 UTC 0528: 1133 UTC 0530 – all day (I have the netcdf file, but can’t find the .raw) 0610: 1842-1942 UTC 0629: 0916-0917, 2014-2049 UTC 0712: 1805-1852 UTC (power outage) 0713: 0843, 2014-2359 UTC (power outage) 0714: 0000-0750, 0821 UTC (power outage) 0716: 0136-1300 UTC 0717: 1817-1821, 2138-2236 UTC (power outage) 0719: 1512-1529, 1755 UTC 0814: 2107-2131 UTC (power outage) 0912 – 0916 2245 UTC (power outage) 6. Describe any quality-assurance procedures performed on the data: Meteorological Station Data: All variables passed basic quality control measures (i.e. checking for realistic values) and were not changed. There was no missing data for the extent of the field project. Disdrometer Data: Micro-Rain-Radar Data: See processing description above. 7. People involved with sample collection, processing, analysis and/or submission: Leah Campbell William Springmeyer Rene Garreaud (Universidad de Chile, Santiago, Chile) Aldo Viscarra (Universidad de Concepcion, Concepcion, Chile) Aldo Montecinos (Universidad de Concepcion, Concepcion, Chile) Daniel Sebastian Veloso Aguila (Universidad de Concepcion, Concepcion, Chile) Diego Rivera Salazar (Universidad de Concepcion, Chillan, Chile) Justin Minder (University at Albany) Yazmina Rojas (University at Albany) ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: meteorological_station_data_lastrancas.tar.gz ----------------------------------------- Weather station data is available in comma delimited (.dat) format, in five minute and hourly intervals. The two files can be found in the data directory: lastrancas_fivemin_20160516-20161007.txt and lastrancas_hourly_20160516-20161007.txt Each data file includes data for the entire period of collection. Five Minute Data Interval Header: Timestamp (‘YYYY-MM-DD HH:MM:SS’), timestamp number, air temperature (°F), air temperature (°C), relative humidity (%), average wind speed (m s-1), wind direction, wind gust speed (m s-1), battery voltage, panel temperature (°C), five minute precipitation (cm), pressure (mb), total precipitation since last reset (cm) Hourly Data Interval Header: Timestamp( ‘YYYY-MM-DD HH:MM:SS’), timestamp number, pressure (mb), hourly precipitation (cm), and total precipitation since last data collection (in) * These headers are also at the top of each file 1. Number of variables: 7 2. Number of cases/rows: lastrancas_fivemin_20160516-20161007.txt: 41696 lastrancas_hourly_20160516-20161007.txt: 3474 3. Variable List A. Name: Wind speed (m s-1) Description: Mean horizontal wind speed Additional Information: Unit: (m s-1) Sampling Interval: 10 sec Averaging or Reporting Interval: 5 min B. Name: Wind direction Description: Unit vector mean wind direction Additional Information: Unit: N/A Sampling Interval: 10 sec Averaging or Reporting Interval: 5 min C. Name: Max wind gust (m s-1) Description: Maximum instantaneous horizontal wind speed Additional Information: Unit: (m s-1) Sampling Interval: 10 sec Averaging or Reporting Interval: 5 min D. Name: Air temperature (°C) Description: Mean air temperature Additional Information: Unit: °C Sampling Interval: 10 sec Averaging or Reporting Interval: 5 min E. Name: Relative humidity (%) Description: Mean relative humidity Additional Information: Unit: % Sampling Interval: 10 sec Averaging or Reporting Interval: 5 min F. Name: Pressure (mb) Description: Instantaneous pressure Additional Information: Unit: mb Sampling Interval: 10 sec Averaging or Reporting Interval: 5 min G. Name: Precipitation (cm) Description: Total interval snow water equivalent Additional Information: Unit: cm Sampling Interval: 10 sec Averaging or Reporting Interval: 5 min 4. Missing data codes: N/A - No missing data. 5. Specialized formats of other abbreviations used N/A ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: parsivel_disdrometer_data.tar.gz ----------------------------------------- Data is available in one .txt file for each day (UTC), where each 10 second interval is represented by a line such as this one: 12.09.2016;00:02:00;6.614;90.55;58;33.104;1901;19060;189;2;0.00;23.7;61.780;0;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; ;;;;;;;;;;;;;;;;;;;;;;;;;;;1;;1;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;1;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; ;;;;;;;;;;;;;;;;;;;;;;3;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;2;2;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;2;8;;;;1;;;;;;;;;;;;;;;;;;;;;;;;;;1;1;3;1;; ;;;;;;;;;;;;;;;;;;;;;;;;;;;;2;7;1;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;4;5;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;3;11;2;;;;;;;;;;;;;;;;;;;;;;;;;;;; 1;;2;5;20;2;1;;;;;;;;;;;;;;;;;;;;;;;;;;;3;1;;6;14;1;;;;;;;;;;;;;;;;;;;;;;;;;;;;;1;3;12;17;1;;;;;;;;;;;;;;;;;;;;;;;;;;;;;1; 1;8;4;4;;;1;;;;;;;;;;;;;;;;;;;;;;;;;;;1;3;7;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;3;2;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;1;;;;;;;;;;;;;;;;;; ;;;;;;;;;;;;;;1;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; ;;;;;;;;;;; The header for the terms before is as follows: Date (DD.MM.YYYY); Time (HH:MM:SS); Rain intensity (mm h-1); Rain amount accumulated (mm); Weather code (WaWa); Reflectivity (dBZ); MOR Visibility (m); Signal amplitude of laserband; Number of detected particles; Temperature in sensor (°C); Heating current (A); Sensor voltage (V); Kinetic Energy; Snow intensity (mm h-1); These variables are all derived by the Parsivel software from the raw dropsize spectrum. Denotes the beginning of the dropsize spectrum for that 10s period. Contains 1024 entries, representing a 32x32 matrix of dropsize vs. velocity following the arrays below. The 1st entry of the 1024 is the 1st dropsize bin and the 1st velocity bin, the 32nd entry is the 32nd dropsize bin and the 1st velocity bin, the 33rd entry is the 1st dropsize bin and the 2nd velocity bin, etc. If there were no precipitation particles detected, this part of the line will simply state “ZERO”. drop_diameter = [ 0.064, 0.193, 0.321, 0.45, 0.579, 0.708, 0.836, 0.965, 1.094, 1.223, 1.416, 1.674, 1.931, 2.189, 2.446, 2.832, 3.347, 3.862, 4.378, 4.892, 5.665, 6.695, 7.725, 8.755, 9.785, 11.330, 13.390, 15.45, 17.51, 19.57, 22.145, 25.235] velocity_bins = [ 0.05, 0.15, 0.25, 0.35, 0.45, 0.55, 0.65, 0.75, 0.85, 0.95, 1.1, 1.3, 1.5, 1.7, 1.9, 2.2, 2.6, 3, 3.4, 3.8, 4.4, 5.2, 6.0, 6.8, 7.6, 8.8, 10.4, 12.0, 13.6, 15.2, 17.6, 20.8] 1. Number of variables: 12 2. Number of cases/rows: One file per day, 8629 entries per file. 3. Variable List A. Name: Rain intensity (mm h-1) Description: Rain intensity during measurement interval. B. Name: Rain amount accumulated (mm) Description: Estimated accumulated rain during measurement interval, derived from rain intensity. C. Name: Weather code (WaWa) Description: Weather code according to SYNOP WaWa. Using theoretical fall speed / particle size relations, the Parsivel produces a precipitation type. Possibilities include: No Precipitation (00), Drizzle (51-53), Drizzle with rain (57-58), Rain (61-63), Rain/drizzle with snow (67-68), Snow (71-73), Snow grains (77), Soft Hail (87-88), Hail (89) D. Name: Reflectivity (dBZ) Description: Estimated radar reflectivity, derived from rain intensity. E. Name: MOR Visibility (m) Description: Meteorological Optical Range (MOR) visibility, derived from rain intensity. F. Name: Signal amplitude of laserband Description: Signal amplitude of the laser strip. G. Name: Number of detected particles Description: Number of particles detected and validated. H. Name: Temperature in sensor (°C) Description: Temperature in sensor (°C) I. Name: Heating current (A) Description: Heating current in sensor (A) J. Name: Sensor voltage (V) Description: Sensor voltage (V) K. Name: Kinetic Energy Description: Kinetic energy of the measured particles, derived from the duration of the signal from particles passing through the laser beam. Unit is J/(m-2*h) L. Name: Snow intensity (mm h-1) Description: Snow intensity during measurement interval. 4. Missing data codes: ZERO - No precipitation particles detected for this 10 sec. period. -9.999 - Reflectivity (dBZ) when no precipitation particles were detected for this 10 sec. period. 5. Specialized formats of other abbreviations used See above for description of data format. ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: mrr_data_raw_concepcion.tar.gz, mrr_data_raw_chillan.tar.gz, and mrr_data_raw_lastrancas.tar.gz ----------------------------------------- Each day has three files. For July 13, for example, the three files are 0713.raw, 0713_050m.raw, and 0713_250m.raw. For the ChOMPS project, all 3 MRRs were programmed to switch between 50 m and 250 m range gates approximately every minute. The MRR records raw Doppler spectra at one range gate at a time, averaged over 10 s, which is output in 10 s intervals in the “.raw” data. Only one file is created per day (dates and times in files and file names are UTC), so it is constantly appended with each new output, and contains both 50 m and 250 m output. For our example, the 0713.raw file contains both range gates. The 0713_050m.raw and 0713_250m.raw contain just the data for the labeled range gate configuration. “Each data block in a raw data file begins with a header line which contains the date, the time and the time zone of the following data block. This line is preceded by the letter T and a colon (T means time). The format of the date/time stamp is YYMMDDhhmmss, which means year, month, day, hour, minute and second with 2 digits each. Date, time and time zone are separated by a space character. The header line is supplemented with the version number of the MRR firmware (following the identifier DVS), the serial number (of the MRR following the identifier DSN), the calibration constant of the MRR (following the identifier CC) and the percentage of valid spectra (following the identifier MDQ). The next data lines contains the measuring heights. It begins with the capital letter M, a colon, the small letter h, two space characters, and an equals sign (M means measured value, h means height). The following numbers (9 digits decimal each) represent the measuring heights in meters. The height line is followed by the line of the transfer function. It starts with the capital character M, a colon, the capital letters T and F and one space character. The rest of that line represents the values of the transfer function for each height step ( 9 digits decimal each). The line of the transfer function is followed by 64 data lines. Each one starts with the capital character M, a colon, the small letter f, and a 2-digit number of the spectra line (0 to 63). The rest of these lines represent the received spectral signal power in engineering units for each height step (9 digits decimal each).” 1. Number of variables: 1 2. Number of cases/rows: Varies for each file (one file per day). 3. Variable List A. Name: Spectral signal power Description: Spectral signal power in engineering units for each height step. See description above. 4. Missing data codes: N/A - Data is only recorded when precipitation is sensed. 5. Specialized formats of other abbreviations used N/A ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: mrr_data_processed_concepcion.tar.gz, mrr_data_processed_chillan.tar.gz, and mrr_data_processed_lastrancas.tar.gz ----------------------------------------- One file per day / range gate configuration. Includes processed output from the Maahn and Kollias (2012) processing method, explained above. 1. Number of variables: 45 2. Number of cases/rows: N/A 3. Variable List A. Name: time Description: Measurement time. Following Metek’ss convention, the dataset at e.g. 11:55 contains all recorded raw between 11:54:00 and 11:54:59 (if delta t = 60s)! Dimensions: (time) FillValue: -9999 Units: seconds since 1970-01-01 B. Name: range Description: Range bins Dimensions: (range) Fill Value: -9999 Units: m C. Name: velocity Description: Doppler velocity bins. If dealiasing is applied, the spectra are triplicated Dimensions: (velocity) Fill Value: -9999 Units: m/s D. Name: velocity_noDA Description: Original, non dealiased, Doppler velocity bins Dimensions: (velocity_noDA) Fill Value: -9999 Units: m/s E. Name: height Description: Height above instrument Dimensions: (time, range) Fill Value: -9999 Units: m F. Name: eta_noDA Description: Spectral reflectivities NOT dealiased Dimensions: (time, range, velocity_naDA) Fill Value: -9999 Units: mm^6/m^3 G. Name: etaMask_noDA Description: Noise mask of eta NOT dealiased, 0: signal, 1:noise Dimensions: (time, range, velocity_noDA) Fill Value: -9999 Units: bool H. Name: eta Description: Spectral reflectivities. if dealiasing is applied, the spectra are triplicated, thus up to three peaks can occur from -12 to +24 m/s. However, only one peak is not masked in etaMask. Dimensions: (time, range, velocity) Fill Value: -9999 Units: mm^6/m^3 I. Name: etaMask Description: Noise mask of eta, 0: signal, 1:noise Dimensions: (time, range, velocity) Fill Value: -9999 Units: bool J. Name: quality Description: A) usually, the following errors can be ignored (no. is position of bit): 1) spectrum interpolated around 0 and 12 m/s 2) peak stretches over interpolated part 3) peak is dealiased 4) first Algorithm to determine peak failed, used backup 5) dealiasing went wrong, but is corrected B) reasons why a spectrum does NOT contain a peak: 8) spectrum was incompletely recorded 9) the variance test indicated no peak 10) spectrum is not processed due to according setting 11) peak removed since not wide enough 12) peak removed, because too few neighbours show signal, too C) thinks went seriously wrong, don\'t use data with these codes16) peak is at the very border to bad data 17) in this area there are still strong velocity jumps, indicates failed dealiasing 18) during dealiasing, a warning was triggered, applied to whole column. Dimensions: (time, range) Fill Value: -9999 Units: bin K. Name: TF Description: Transfer Function (see Metek's documentation) Dimensions: (time, range) Fill Value: -9999 Units: N/A L. Name: Ze_noDA Description: Reflectivity of the most significant peak, not dealiased. Dimensions: (time, range) Fill Value: -9999 Units: dBz M. Name: Ze Description: Reflectivity of the most significant peak Dimensions: (time, range) Fill Value: -9999 Units: dBz N. Name: spectralWidth_noDA Description: Spectral width of the most significant peak, not dealiased Dimensions: (time, range) Fill Value: -9999 Units: m/s O. Name: spectralWidth Description: Spectral width of the most significant peak Dimensions: (time, range) Fill Value: -9999 Units: m/s P. Name: skewness_noDA Description: Skewness of the most significant peak, not dealiased Dimensions: (time, range) Fill Value: -9999 Units: m/s Q. Name: skewness Description: Skewness of the most significant peak Dimensions: (time, range) Fill Value: -9999 Units: m/s R. Name: kurtosis_noDA Description: Kurtosis of the most significant peak, not dealiased Dimensions: (time, range) Fill Value: -9999 Units: m/s S. Name: kurtosis Description: Kurtosis of the most significant peak Dimensions: (time, range) Fill Value: -9999 Units: m/s T. Name: peakVelLeftBorder_noDA Description: Doppler velocity of the left border of the peak, not dealiased Dimensions: (time, range) Fill Value: -9999 Units: m/s U. Name: peakVelLeftBorder Description: Doppler velocity of the left border of the peak Dimensions: (time, range) Fill Value: -9999 Units: m/s V. Name: peakVelRightBorder_noDA Description: Doppler velocity of the right border of the peak, not dealiased Dimensions: (time, range) Fill Value: -9999 Units: m/s W. Name: peakVelRightBorder Description: Doppler velocity of the right border of the peak Dimensions: (time, range) Fill Value: -9999 Units: m/s X. Name: leftSlope_noDA Description: Slope at the left side of the peak, not dealiased Dimensions: (time, range) Fill Value: -9999 Units: dB/(m/s) Y. Name: leftSlope Description: Slope at the left side of the peak Dimensions: (time, range) Fill Value: -9999 Units: dB/(m/s) Z. Name: rightSlope_noDA Description: Slope at the right side of the peak, not dealiased Dimensions: (time, range) Fill Value: -9999 Units: dB/(m/s) AA. Name: rightSlope Description: Slope at the right side of the peak Dimensions: (time, range) Fill Value: -9999 Units: dB/(m/s) BB. Name: W_noDA Description: Mean Doppler Velocity of the most significant peak, not dealiased Dimensions: (time, range) Fill Value: -9999 Units: m/s CC. Name: W Description: Mean Doppler Velocity of the most significant peak Dimensions: (time, range) Fill Value: -9999 Units: m/s DD. Name: etaNoiseAve Description: Mean noise of one Doppler Spectrum in the same units as eta, never dealiased Dimensions: (time, range) Fill Value: -9999 Units: mm^6/m^3 EE. Name: etaNoiseStd Description: Std of noise of one Doppler Spectrum in the same units as eta, never dealiased Dimensions: (time, range) Fill Value: -9999 Units: mm^6/m^3 FF. Name: SNR Description: Signal to noise ratio of the most significant peak, never dealiased! Dimensions: (time, range) Fill Value: -9999 Units: dB GG. Name: lat Description: Latitude of the MRR Dimensions: () Fill Value: N/A Units: degrees_north HH. Name: lon Description: Longitude of the MRR Dimensions: (time, range) Fill Value: N/A Units: degrees_east II. Name: MRR_elevation Description: Elevation of the MRR. Dimensions: () Fill Value: N/A Units: m MSL 4. Missing data codes: See Fill Values listed above. 5. Specialized formats of other abbreviations used N/A