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Data access (API key available by registering here):

  • Research license has a 48 h delay in data availability
Data types:

1 Test

2 None

3 Rain

4 Freezing Rain

5 Drizzle

6 Freezing Drizzle

7 Ice Pellets/Sleet

8 Snow and/or Graupel

9 Mixed Rain and Snow

10 Mixed Ice Pellets and Snow

11 Mixed Rain and Ice Pellets

12 Graupel

13 Pea (0.25 in.)

14 Half-inch (0.50 in.)

15 Dime (0.75 in.)

16 Quarter (1.00 in.)

17 Half Dollar (1.25 in.)

18 Ping Pong Ball (1.50 in.)

19 Golf Ball (1.75 in.)

20 Hen Egg (2.00 in.)

21 Hen Egg+ (2.25 in.)

22 Tennis Ball (2.50 in.)

23 Baseball (2.75 in.)

24 Tea Cup (3.00 in.)

25 Baseball+ (3.25 in.)

28 Grapefruit (4.00 in.)

30 Softball (4.50 in.)

32 Softball++ (>=5.00 in.)

33 Lawn furniture or trash cans displaced; Small twigs broken

34 1-inch tree limbs broken; Shingles blown off

35 3-inch tree limbs broken; Power poles broken

36 Trees uprooted or snapped; Roof blown off

37 Homes/Buildings completely destroyed

40 River/Creek overflowing; Cropland/Yard/Basement Flooding

41 Street/road flooding; Street/road closed; Vehicles stranded

42 Homes or buildings filled with water

43 Homes, buildings or vehicles swept away

44 Mudslide

45 Dense Fog

46 Blowing Dust/Sand

47 Blowing Snow

48 Mixed Freezing Rain and Ice Pellets

mPING Data Groupings:
  • Each of the following groupings is used to define a message type "MPING_TYPE" for the following types:
    • RAIN: 3, 5, 9, 11
    • SNOW: 8, 9, 10
    • FRZR (Freezing Rain): 4, 6, 48
    • ICEP (Ice Pellets): 7, 10, 11, 12, 48
    • NONE: 2
Collapse the mPing data into four classes to match that coming from models (SN, RA, PL, and FZRA)
  • Overall, agreement is slightly higher for the PL-biased method (see Elmore et al.)
RA-biased method:
  • RA
    • drizzle
    • rain
    • mixed rain and snow
    • mixed rain and ice pellets
  • SN
    • snow
    • mixed ice pellets and snow
  • FZRA
    • freezing drizzle
    • freezing rain
  • PL
    • ice pellets-sleet
PL-biased method
  • RA
    • drizzle
    • rain
  • SN
    • snow
    • mixed rain and snow
  • FZRA
    • freezing drizzle
    • freezing rain
  • PL
    • ice pellets-sleet
    • mixed ice pellets and snow
    • mixed rain and ice pellets
From Heather Reeves (email 23 Feb 2016):
I've attached an ascii file with the mPING observations from Dec 2012 to 31 Mar 2015.  It's comma separated with the columns indicating the following:
ID number, m/dd,yyyy, hh:mm,type, lat, lon
I have some cautionary notes regarding the use of mPING obs (this a paper that I'm preparing as we speak).  mPING users over report instances of PL and underreport FZRA.  There is also an underreporting of RA (probably b/c the public is not as motivated to report less exotic forms of precip).  The biggest issue is that you cannot assess the temporal variability with mPING and this has a non-negligible effect on the types of validation stats I've considered. The paper isn't done, but if you want to see what I've recorded so far before committing to using this dataset, I'm happy to share.
Access to mPING data API granted to John and Jamie (27 Feb 2016):
Link to mPING API information:
Location of mPING data: tuvok:/d1/TAIWIN_VX/data/mPING/YYYY/YYYYMM/mping_YYYYMMDD.txt
Python script to pull data: tuvok:/d1/TAIWIN_VX/scripts/
Usage for the mPING script:
    USAGE: ./ beg [end], YYYYMMDD format

It takes 1 or 2 arguments, both in YYYYMMDD format.  It'll pull mPING data for each day specified.  The reports are stored in an 11-column format that could be used for as input to the MET ascii2nc tool.  If we need to reformat, we can easily regenerate this data.  The script supports "paging", so it'll automatically grab all the reports, even if the count exceeds the upper limit of 5000.
I've pulled all the data since 20160101, and I have it set to run via crontab at 6am every day pulling all the data from 3 days prior:
Tuvok crontab entry:
# Pull mPING data for 3 days prior
00 06 * * * /d1/TAIWIN_VX/scripts/ `date -d '-3 day' +\%Y\%m\%d` > /d1/TAIWIN_VX/data/mPING/pull_mping_cron.log 2>&1


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