Development of TP Regional Precipitation Datasets and Products:

need and progress

Daqing Yang

National Hydrology Research Centre (NHRC)

Environment Canada (EC), Saskatoon, Canada

Yinsheng Zhang, Yingzhao MaInstitute of Tibetan Plateau Research

Chinese Academy of Sciences

3rd TPE Workshop, Sept 1, 2011

Need for P Data and Products

• Climate: key variable to define climate and its change • Glacier: accumulation and mass balance • Hydrology: flood/drought, water budget, and input to models• Snowcover: mass/energy balance, avalanche• Ecosystem: water supply, NDVI - P relation, vegetation change

vs. precip impact • Human: transportation, recreation, cultural

• 2nd TPE workshop report– 1# product: Precip (when, where, how much, form, and trend) – TPE atlas, i.e. basin/regional hydroclimatology

Meteorological Stations over TP

• Network: density and distribution

• Data quality:

- space/time discontinuity

- obs biases

Biases and Correction Method

  Pc = K Pg + Pw + Pe + Pt

Pc- corrected precipitation

Pg - gauge-measured precipitation

Pw - wetting loss

Pe - evaporation loss

Pt - trace precipitation

K - correction coefficient for wind-induced undercatch

Gauge Precipitation

(Yang et al, 1991)Daily Correction Coefficient (K)

Bias-corrected Precipitation

NASA Website TRMM 0.25°3B42_V6 Product

Reliability Evaluation

TRMM Precipitation

Homogenized and correctedPrecipitation

Dataset

FTP Download

DEMPart Ⅱ

Part Ⅰ

Part Ⅲ

Project Flowchart: Homogenized and Corrected Precipitation Dataset over TP

(Goodison et al., 1998)

WMO Solid Precipitation Measurement WMO Solid Precipitation Measurement Intercomparison, manual and auto gauges Intercomparison, manual and auto gauges

WMO Double Fence International WMO Double Fence International ReferenceReference

(DFIR) for Solid Precipitation(DFIR) for Solid Precipitation

Secondary reference

CanadaBarry GoodisonPaul LouieJohn MetcalfeRon Hopkinson

ChinaDaqing YangErsi KangYafen Shi

CroatiaJanja Milkovic

DenmarkHenning MadsenFlemming VejenPeter Allerup

FinlandEsko ElomaaReijo HyvonenBengt TammelinAsko TuominenS. Huovila

IndiaN. Mohan RaoB. BandyopadhyayVirendra KumarCol K.C. Agarwal

GermanyThilo Günther

JapanMasanori ShirakiHiroyuki OhnoKotaro YokoyamaYasuhiro KominamiSatoshi Inoue

NorwayEirik Førland

CRN modified DFIR

• Intercomparison was the result of Recommendation 17 of the ninth session of the CIMO-IX.

• Started in the northern hemisphere winter of 1986/87.

• Field work carried out at 26 sites in 13 Member countries for 7 years

• Final report WMO-TD no. 827 published in 1998

Barry GoodisonChairman, International Organizing Committee

WMO Solid Precipitation Measurement Intercomparisonsites and people

RomaniaViolete Copaciu

Russian FederationValentin GolubevA. Simonenko

SlovakiaMiland Lapin

SwedenBengt Dahlstrom

SwitzerlandBoris SevrukFelix BlumerVladislav Nešpor

UKJ. FullwoodR. Johnson

USARoy BatesTimothy PangburnH. GreenanGeorge LeavesleyLarry BeaverClayton HansonAlbert RangoDouglas EmersonDavid LegatesP. Groisman

WMOKlaus SchulzeStephan Klemm

Daily catch ratio vs. daily wind speed

CATCH RATIO VS. WIND SPEED, SNOW, DFIR > 3.0mm, 2 WMO SITES

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WIND SPEED AT GAUGE HEIGHT (m/s)

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Canadian Nipher NWS 8" AlterNWS 8" unsh Hellmann unshTretyakov

WMO Intercomparison Study Results:WMO Intercomparison Study Results:

Catch Efficiency vs. Wind for 4 most widely used Catch Efficiency vs. Wind for 4 most widely used gaugesgauges

Daily time scale Daily time scale

Barry Goodison, Paul Louie, and Daqing Yang,the 14th Professor Vilho Vaisala Awardin 1998

• About 30 papers in international journals

• National reports

• WMO TD, No- 827, 1998

Report and Publication

Recommendations from the WMO Intercomparison Study

• WMO correction methods (available for different types of gauges and for different types of precipitation and various time intervals) should be adopted and applied to current and archived data;

• both measured and corrected precipitation data should be reported and archived;

• trace precipitation should be treated as a non-zero event; effort to determine mean trace amount is needed in Arctic conditions;

• additional wind speed measurements be taken at the level of the gauge orifice and hourly mean wind data be archived in order to correct for wind-induced undercatch;

• use of heated tipping-bucket gauges for winter precipitation measurement should be carefully assessed; their usefulness is severely limited in regions where temperatures fall below 0C for prolonged periods of time;

• timing and type of precipitation be recorded by automatic instruments in order to conduct the correction on the basis of precipitation event.

a) Pm (mm) b) Pc (mm) c) CF

Mean Gauge-Measured (Pm) and Bias-Corrected (Pc) Precipitation, and Correction Factor (CF) for January

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Pc (mm)

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CF

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1.6 - 1.7

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1.8 - 1.9

1.9 - 2.3

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Pm (m m )

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90 - 330

• Total 4827 stations located north of 45N, with data records longer-than 15 years during 1973-2004.

• Similar Pm and Pc patterns – corrections did not significantly change the spatial distribution.

• CF pattern is different from the Pm and Pc patterns, very high CF along the coasts of the Arctic Ocean.

Impact of Bias-Corrections on Northern Hydrology: CLM3 simulations with/without P corrections, 1973-04 5~25%

Annual precip bias-corrections in China, 1951-1998 (Ye et al. 2003, JHM)

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2800Corrected annual mean precipitation (mm)China, 1951-98

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Trend Comparison, measured vs. corrected annual precipitation, 670 stations in China,

1951-98

y = 1.08x - 4.9323R2 = 0.9442

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Trend in measured annual precipitation (mm/10a)

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Trend in measured annual precipitation (%/10a)

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Average trend for all stations:

+ 0.5mm/10a for measured precip

- 4.5mm/10a for corrected precip

Water balance summary for the Yellow river source, 1958-2001

Dataset Runoff (mm)

Precipitation (mm)

Precipitation difference

(%)

Evaporation (P-R) (mm)

Evaporation difference

(%)

Runoff coefficient

Measured 502. 4 0 335.1 0 0.33

Corrected 167. 3 599. 9 19 432.6 29 0.28

EAR-40 171. 5 537. 2 7 365.8 9 0.32

• P = R + E or E = P – R

• P undercatch -> E underestimation ???

Gauge Precipitation

(Yang et al, 1991)Daily Correction Coefficient (K)

Bias-corrected Precipitation

NASA Website TRMM 0.25°3B42_V6 Product

Reliability Evaluation

TRMM Precipitation

Homogenized and correctedPrecipitation

Dataset

FTP Download

DEMPart Ⅱ

Part Ⅰ

Part Ⅲ

Project Flowchart: Homogenized and Corrected Precipitation Dataset over TP

Yearly Mean Bias-Corrected Precipitation in TP

Yearly-Correction Coefficient Distribution in TP

Sample of TRMM product• weekly accumulation, TRMM-precipitation estimation in Asia,• global 0.25° * 0.25° grid over the latitude band 50°N-S • about seven hours of observation time.

• Key question: TRMM winter data for solid and mixed precip over the mountain regions, i.e. TPE

Geonor-DF

Bratt’s Lake Intercomparison Facility/Smith

DFIR

Barrow, UAF

DFIR, Mar 3/03

Barrow, UAF Wyoming snow fence, Mar/03 Barrow, UAF DFIR, Mar 03

WMO Solid Precipitation Inter-Comparison

Experiment (WMO-SPICE)

EC Snow Workshop,TorontoDec 01, 2010

Rodica Nitu

CIMO-XV (Sept 2010)• An instrument intercomparison for solid precipitation measurements at AWS: a

priority!  

• WMO-SPICE: WMO Solid Precipitation Instrument Intercomparison Experiment

• Canada committed to a leadership role if other Members participate and share the work

• Support and commitment expressed: China, Finland, Japan, New Zealand, Switzerland, Russian Federation, and USA.

• In CIMO, SPICE positioned in the context of WIGOS, EC-PORS, GCW.

WMO-SPICE: Proposed objectives

• Evaluate the performance and configuration (catching, non-catching type, instrument & shield) of measurements in field conditions;

• Develop multi-parameter algorithms to improve AWS precipitation data;

• Develop adjustment procedures of systematic errors;

• Establish a field reference standard using automatic gauges;

• Develop long-term capacity to support validation of satellite measurements (e.g. Global Precipitation Measurement);

• Develop comprehensive datasets to support future research objectives;

• Provide feedback to manufacturers;

• Pilot project for WIGOS, EC-PORS, GCW.

Proposed Ancillary Measurements

• Radar – for horizontal (PPI scans) and vertical profiling (RHI or Vertical scans) for variation of precipitation. Dual-pole for precipitation typing.

• Radiometer – determine the presence of liquid water (determine if particles are rimed).

• Wind measurements (3D anemometers) – for turbulence, gustiness, at sensor height.

• Precipitation Type Sensors – present weather sensors, intensive human observations.

• Temperature and humidity – point and profiling, to determine habit types

• Particle size, particle density and shape information – for aerodynamic collection efficiency issues;

• Snowpack properties – snow depth, snow morphology, snow (freshly fallen and snow pack) density;

• Lidar for cloud properties;

• Upper Air soundings for air mass stability.

Summary • Large biases/errors in historical gauge precipitation data

• Bias corrections necessary, using WMO methods and station meta data/info

• Impacts of precip bias corrections – changes in max P, mean, variation, and trend– SWE, snowmelt runoff, river flow, water and energy budget

• Needs:– compatibility among gauge observations, manual vs. auto gauges (including TPE networks/stations)– test of auto gauges and instruments – WMO/SPICE– validation of RS P/snowfall data over the cold regions – TPE P working group/project???