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Outline Detecting occurrence and quantifying amount of precipitation Sources of errors Reading Assignment: Section 4.1.

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Presentation on theme: "Outline Detecting occurrence and quantifying amount of precipitation Sources of errors Reading Assignment: Section 4.1."— Presentation transcript:

1 Outline Detecting occurrence and quantifying amount of precipitation Sources of errors Reading Assignment: Section 4.1

2 Precipitation Quiz/Assignment Work through snowpack COMET module: Enroll. Complete quiz. Have quiz results ed to Use this link: https://www.meted.ucar.edu/afwa/snowpack/https://www.meted.ucar.edu/afwa/snowpack/ A module on precipitation (not required to watch) ement/ ement/ Another good webcast (not required) –

3 The Problem Precipitation: Total depth to which a flat, horizontal surface of known area would have been covered assuming no water loss by melting, runoff, or evaporation Measuring precipitation is one of the hardest environmental measurements to make due to: It’s spatial and temporal variability Impact of other environmental conditions, particularly wind Advantageous to have manual as well as automated observations

4 Snowfall in Tughill Plateau deo/sandycreek.mov Courtesy J. Steenburgh

5 What is Goal? Detecting occurrence of precipitation Liquid water equivalent of all hydrometeors: rain, snow, graupel, hail Measuring snow: – Incremental depth and its snow water equivalent – Crystal type of new snow – Total Depth and its snow water equivalent – Crystal type of existing snowpack

6 Occurrence of Precipitation Often tied with obstructions to visibility Campbell: Measuring precipitation type (rain vs. snow) hydrometeor size, and rate (light vs. heavy); not measuring amount Using laser and detecting scattering and optical differences in horizontal and vertical planes

7 Precipitation Rate vs. Accumulation Precipitation: Total depth to which a flat, horizontal surface of known area would have been covered assuming no water loss by melting, runoff, or evaporation Precipitation rate: precipitation mass per unit time per unit area: R= M w /  w [kg/(m 2 s) / kg/ m 3 ] Precipitation rate in m/s; mm/hr; in/day, etc. So, when someone says it rained an inch, it is always necessary to know the time interval over which that happened as well

8 Snow Water Equivalent

9 Manual Measurement CoCoRAHS training: nt.aspx?page=training_slidesho ws CoCoRAHS training: nt.aspx?page=training_slidesho ws Measure precipitation at fixed temporal intervals Inner measuring cylinder amplifies signal while reducing catch errors Tremendous sampling issues

10 Automated Measurements Tendency to focus on issues related to mechanics of sensors at expense of worrying about how representative the observations may be for precipitation beyond observing site

11 Automated Sensors Volumetric: Tipping bucket Weighing – Pressure (Belfort,Noah) – Vibrating wire (Geonor) Others – Optical – Hot plate

12 Automated Sensors

13 SNOTEL: Weighing Gauge and Snow Pillow Low resolution: 0.1 inch water equivalent Subject to temperature fluctuations Weighing gauge

14 SNOTEL Snow Pillow

15 Vibrating Wire The precipitation collected in the container are weighed with a vibrating wire load sensor, which gives a frequency output. The frequency will be a function of the applied tension on the wire, i.e. from this, the amount of precipitation can be computed. The frequency is recorded as a square- shaped 0-5 V signal

16 Hot Plate sensor provides real time snow and liquid precipitation rates sensor head consists of two isolated plates warmed by electrical heaters During storms, it measures the rate of rain or snow by how much power is needed to evaporate precipitation on the upper plate and keep its surface temperature constant The second plate, positioned directly under the evaporating plate and heated to the same temperature as the top, is used to factor out cooling from the wind.

17 Snow Depth The sensor measures the distance from the sensor to a target. The sensor works by measuring the time required for an ultrasonic pulse to travel to and from a target surface. An integrated temperature probe with solar radiation shield, provides an air temperature measurement for properly compensating the distance measured. An embedded microcontroller calculates a temperature compensated distance and performs error checking. Both distance and air temperature can be output as an analog signal between 0 to 2.5 Volts or 0 to 5 Volts. Accurate measurement of snow depth poses many difficult problems

18 TugHill Plateau From J. Steenburgh

19 Measuring Snow Depth

20 Sources of Error Representativeness – Sampling errors can be + or – Wind Evaporation- dry gauge delays detection of onset Splash/capping/plugging/dew Tipping bucket underestimation at high rain rate Temperature sensitivity to weighing gauge Automating precipitation detection

21 Sources of Error Exposure – Turbulent flows lead to undercatch – Worse for snow vs. rain – Problems increase as wind speed increases

22 B.E. Goodison, P.Y.T. Louie, and D. Yang (1998)

23 Rain Rule of thumb: 1% loss for every 1 mph increase in wind speed, or 2.2% for every 1 m/s increase Uncertainty associated with drop sizes & gauge location Best measurements in windy conditions: – Large drops – Gauge near the ground – Shielded gauges Poorest measurements – Small drops/drizzle drops – Elevated gauges

24 Wind Shield Type Double Fence Intercomparison Reference (DFIR) Single/Double Alter shields Natural shielding by trees

25 Climate Reference Network Station Wyoming for National-Level Climate Monitoring (Red Canyon Nature Conservancy, Wyoming)

26 New Historical Climatology Network Station for Regional-Level Climate Monitoring (Greenville, AL)

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29 Impact of Shields Shielded gauges, on average, measure 20–70% more snow than unshielded gauges (Yang et al. 1999) The use of wind shields on precipitation gauges has introduced a significant discontinuity into precipitation records, particularly in cold and windy regions This discontinuity is not constant and it varies with wind speed, temperature, and precipitation type.

30 B.E. Goodison, P.Y.T. Louie, and D. Yang (1998)

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32 Nešpor, V. (1993)

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35 Small Double Fence Intercomparison Reference (SDFIR)

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37 CoCoRaHS Community Collaborative Rain Hail and Snow Network

38 Summary Gauge reports are fine for well-maintained, optimally located gauges in light winds Rain gauges do not necessarily provide good spatial resolution Significant underestimation error in strong winds Gauge undercatch affected by variability in wind and hydrometeor type High wind: greater inaccuracy in gauge data than in radar data (where good radar data)


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