Analysis of Record for Calibration (AORC) Temperature and … · 2019. 8. 20. · Analysis of...

Analysis of Record for Calibration (AORC)

David Kitzmiller, Ziya Zhang, Wanru Wu, Nathan Patrick, Xuning Tan

Major contributions from Dennis Miller, Yuxiang He,Yu Zhang, James Ward, Xiaoshen Li, Xia Feng , Lubak Kassa,

Gustavo Valenzuela, Sanian Gaffar, Hank Herr

Office of Water PredictionNational Weather Service, NOAA

14 August 20181

Today’s Presentation

• Motivation for the project• Basic properties of the archive

– Inputs, outputs– Temporal/spatial coverage

• Statistical properties of temperature/precipitation relative to NLDAS-2

• Experiments in wind downscaling, satellite radiation blending

• Projects using AORC data• Updating system• Challenges for dissemination

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Motivation

• Considerable effort involved in data assembly for hydrologic model re-calibration

• Many gridded meteorological inputs are already available

• Aim to produce a single source of meteorological data for basin-average precipitation and temperature, and grids for distributed models

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AORC Properties (CONUS-region)

• Period of record 1979-near present• Coverage for superCONUS region:

– From Rio Conchos to Columbia basins– 23-53° N

• Final resolution 30” (match PRISM climatology grids)

• Includes all sensible weather elements• Temperature/precipitation constrained toward

1981-2010 climatology• NetCDF4 storage, some available in grib2

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Climatology Input Grids:• Parameter Regression on Independent Slopes

Method (PRISM)– CONUS (publicly available)– British Columbia (Pacific Climate Impacts Consortium)– 30 arc-second grid mesh

• NCEI (formerly NCDC) – Vose et al:– North America, 17⁰ - 54⁰ N, 120 arc-second

• Mexico inputs derived by OWP (Nathan Patrick)– Used ANUSPLIN package; SMN and NCEI station inputs– 30 arc-sec

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AORC Temperature/PrecipitationInputs

• Temperature:– Livneh et al (2015) daily Tmax/Tmin– (ftp://livnehpublicstorage.colorado.edu/public/Livneh.2016.Dataset/)

– NLDAS2 hourly temperature– PRISM 1981-2010 climatology 0.008° grid mesh

• Precipitation:– Livneh monthly precipitation totals– NLDAS2, Stage IV daily precipitation totals– Other radar-based inputs and CMORPH satellite– Climate Forecast System Reanalysis hourly

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Pressure/Humidity/Wind/RadiationInputs

• From NLDAS2:– LW↓ (infrared)– SW↓ (solar)– Specific Humidity– Pressure– Wind Vectors

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Challenges

• Cross border issues in precipitation analyses• Resolving timing mismatch between

daily/subdaily sources• Large time-space variations in quality of

precipitation estimates• Subdaily precipitation sources drop in/out• Long-term constraint to climatology is needed

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Temperature Distribution Approach

• Mean temperature correction:– Maintain running 48-h average mean NLDAS2 and

mean Livneh temperatures– Mean Livneh temperature: (TLIVmax+TLIVmin)/2– Mean correction = TLIVmean - TNLDASmean

• Diurnal temperature range correction:– Expand/contract NLDAS2 min/max to a

temporally smoothed Livneh daily range• Final correction is weighted mean of

mean/range corrections12

Example: St. Louis, 6-10 Feb 2002:Blue Solid:NLDAS2 Dashed Black: Obs

Red Solid:Diurnal Range Correction (AORC)

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Example: St. Louis, 6-10 Feb 2002:Blue Solid:NLDAS2 Dashed Black: Obs

Red Solid:Running Mean Correction (AORC)

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NLDAS2 and AORC TDTR2100 UTC 6 Feb 1979

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NLDAS2 AORC TDTR

NLDAS2 and AORC TDTR; Rocky Mountains2100 UTC 6 Feb 1979

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NLDAS2 AORC TDTR

3h Temperatures00-00UTC 02-03 Feb 1979

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Precipitation Distribution Approach• Monthly total precipitation from Livneh et al

(2015) dataset• Daily totals from:

– NLDAS2 1979-2009– StageIV + NLDAS2 2010+

• Hourly totals based on disaggregating daily totals:– StageII StageIV (radar) when available 1996+– CMORPH– Climate Forecast System Reanalysis (CFSR)– Applied Manually Digitized Radar and CFSR prior to

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1-h Precipitation27 July 2011

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AORC/NLDAS2 Comparison

• North American Land Data Assimilation System v2 (NLDAS2) has been successfully used in NWM calibration

• AORC, StageIV, and NLDAS2 errors relative to NLDAS2

• Mean monthly temperature• Total monthly precipitation• Focus on 2007-2016 National Water Model

calibration period

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Reference Monthly Precipitation/Temperature

• GHCN: Long-record sites in CONUS, Canada, Mexico back to early 20th century– https://www1.ncdc.noaa.gov/pub/data/ghcn/

• Have 125 sites, mostly in CONUS• Total precipitation, mean monthly temperature

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Methods

• Published point GHCN values compared with NLDAS2, StageIV, and AORC values extracted from grids, bilinear interpolation– Note no downscaling applied to NLDAS2 data

• Statistics from each station time series:– Linear correlation year-over-year, by month– RMSE for year-over-year time series– Mean values

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JAN

OCT

JUL

APR

TEMPERATURE, RMSE COMPARISONS

Notes on Temperature

• Many GHCN sites were used in the Livneh et al (2015) dataset that drives AORC

• Overall bias generally within 1.5°• High RMSE points in AORC are generally over

Canada/Mexico

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JAN

OCT

JUL

APR

MONTHLY PRECIPITATION, RMSE COMPARISONS

Notes on Precipitation

• Many GHCN sites were used in the Livneh et al (2015) dataset that drives AORC

• Overall bias generally within 5mm• High RMSE points in AORC are generally over

Canada/Mexico• StageIV has some seasonally-dependent

biases, especially in the early part of the record < 2010

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ConclusionsAORC/NLDAS Temperature/Precip Comparison

• In terms of accuracy, we generally got improvement over NLDAS2– Assume terrain downscaling is relatively minor

effect in the majority of sites, which are in locally flat terrain

• AORC had few major errors in terms of long-term biases– Some concern with Mexico sites – how

representative?

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Experiments in Downscaling Winds• Noted that NLDAS2 winds model broad-scale

features related to terrain and climatology• Little relationship to small-scale terrain

features < 40-km wavelength• We derived high-resolution wind speed

climatology from NCEI station values, terrain• Attempted to apply wind speed bias

corrections to NLDAS2 original winds

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NLDAS2 Mean WindsJanuary 18UTC

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Climatology: High Plains, Rocky MountainsJanuary 18UTC

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Values scaled by 10x

January Bias Wind Speed Correction Factors 00/06/12/18 UTC

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Values scaled by 100x

Experiments in Downscaling Winds• Corrections did improve long-term mean wind

speed• However, could not demonstrate case-by-case

improvement at individual observing sites– Possibly due to directional dependence of terrain

acceleration/deceleration of winds

• Deferred wind downscaling until a more sophisticated method is identified

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SW↓ from Satellite Sources

• GOES Solar Insolation Products (GSIP) available after 2014

• Evaluation showed SW↓ generally superior to NLDAS2– Relative to SURFRAD observations

• GSIP LW ↓ was not an improvement on NLDAS2

• Applied GSIP SW↓ when available, and in real-time updates

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GSIP and NLDAS2 vs. SURFRAD SW↓

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SW↓ from Satellite Sources

• Generation/transmission of the “GSIP” product suite from the next-generation GOES stopped in 2018

• Replacement product from ABI has much larger mesh (0.25° vs. nominal 4-km)

• Immediate and long-term problems: – Real-time replacement– Handling 2014-2017 period of record

• Current solution: – Insert NLDAS2 SW↓ rather than new ABI product– Replace GSIP with NLDAS2 for 2014-2017– Best hope for continuity in the products

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SW↓ from NLDAS21800 UTC4 Feb 2018Nominal 0.125° mesh

SW↓ from ABI-GSIP1802 UTC4 Feb 2018Nominal 0.25° mesh

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AORC Updating System• Utilizing new data sources for 2016 to present• URMA for temperature, humidity, winds• NLDAS2 for superCONUS LW↓, SW↓ • Global Data Assimilation System (GDAS) for

OCONUS LW↓, SW↓ • StageIV precipitation for CONUS, Alaska, Puerto

Rico• NLDAS2 and Climate Prediction Center

precipitation for Canada, Mexico• Statistical adjustment of precip and URMA

temperature toward AORC climatology up to 2015

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