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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