An evaluation of observationally based, high resolution gridded data sets over the continental

United States

Ruben Behnke – UMT Missoula, UW MadisonAndrew Allstadt – UW Madison

Jared Oyler – UMT MissoulaSteve Vavrus – UW Madison

7/13/1995 2/2/1996 10/3/2000Tmax Tmax Precipitation

HOT day in Madison Cold day in Madison Very Wet day Miami

DataSetTMax_Obs

(°C)Data(°C)

TMax_Obs (°C)

Data(°C)

Precip_Obs (mm)

Data (mm)

DayMet 38.3 35 -25.6 -20.5 319 40JohnA 38.3 35.48 -25.6 -17.1 319 222.5Livneh 38.3 37.78 -25.6 -25 319 258.8Maurer 38.3 37.2 -25.6 -23.7 319 124.9NLDAS2 38.3 36.74 -25.6 -27.1 319 151.5TopoWx 38.3 35.38 -25.6 -20 319 57.34 (CPC)

1st Motivation: Different data sets can provide much different data for the same query. Here’s a couple specific examples of where/when data sets differ from each other and obs…

2nd Motivation: After having been asked to provide climate data for a myriad of research topics, I decided a formal analysis of which data set to either recommend or provide to the end user is needed.

3rd Motivation: A formal analysis is needed in order to determine what needs to be improved, possible new research paths, etc.

Project Focus and Path1) Increasing number of gridded, daily data sets out there, but which

one to use?

2) Focus on END USERS needs (how well gridded data match station data only)! – not designed to take into account interpolation

algorithms, stations included, etc.

3) Consider ‘Observations’ to be actual GHCN/COOP/etc. station data

4) Focus on ‘extreme’ indices (CLIMDEX) as they are the hardest to model, but BIOCLIM indices, as well as several other measures are

being calculated

Large scale; ~7000 precipitation, ~5000 temperature stations

Some results… (from a first study using 119 stations from around the country)

Introductory CLIMDEX figures – these are meant to indicate variation among data setsAll examples are for Madison, WI.

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# Nights > 20° C

ObsJohnADayMetLivnehMaurerNLDAS2TopoWx

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# Days > .25 mm (0.01") Precipitation

ObsJohnADayMetLivnehMaurerNLDAS2CPC

CLIMDEX, continued…

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# Frost Days/Year

ObsJohnADayMetLivnehMaurerNLDAS2TopoWx

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# Days/Year > 25° C

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How well does each data set model daily Precipitation from 1981 – 2010?(based on ‘average daily U.S. Precipitation’ derived from 119 stations)

HistogramsBLUE – StationGREY - MODEL

Conditional Quantiles- For each bin in the histogram, the median, 25th/75th, and 10th/90th percentiles are calculated.

Why is there a spike in 4 of these data sets for

this bin?

How well does each data set model daily Precipitation from 1981 – 2010?(based on ‘average daily U.S. Precipitation’ derived from 119 stations)

Taylor DiagramShows the RMSE, correlation, and

standard deviation of a modeled data

series relative to an observed data series

ExampleThe observed RMSE is 0, correlation is 1,

and standard deviation is 1 (by

definition)

Relative to the observed data,

DayMet’s correlation is 0.79, RMSE is 0.57,

and normalized standard deviation is

0.96

How well does each data set model daily TMax from 1981 – 2010?(based on ‘average daily U.S. Tmax’ derived from 119 stations)

How well does each data set model daily TMax from 1981 – 2010?(based on ‘average daily U.S. Tmax’ derived from 119 stations)

So, when averaging across many stations, the data sets do a good job for temperature. But precipitation is more difficult and this

showed in the Taylor and quantile diagrams.

What happens when we look at individual stations?Precipitation

(observed daily data vs daily data corresponding to grid cell where station is located)

Maximum Temperature (observed daily data vs daily data corresponding to grid cell where station is located)

Let’s take a look at Madison, WI in more detail.Precipitation Maximum Temperature

Individual grid cells, even those with a first order station used in the

interpolation, are much harder to model

There’s that spike in the downscaled

data again!

May be stating the obvious, but…1) Fairly easy to get good temporal and spatial averages, totals,

etc. Much more difficult to model daily values at individual grid cells (even those which contain a station).

2) Precipitation is much more difficult to model than temperature.

3) Higher resolution does not necessarily equal better data.4) Choosing a graphic or statistic to: a) analyze data and/or b) communicate results isn’t straightforward as user’s needs vary (extremes vs. means, station vs. region, etc.)

Some First Results5) The “best” data set tends to vary by location and variable6) An “overall best” data set… Ben Livneh (??) – also the newest

7) Much more specific results coming…

Future work…

• Expand analysis to 7000 precipitation, 5000 temperature stations

• Aggregate results spatially by topography, ecoregion, etc.

• Add new/upcoming data sets (daily PRISM, Dan McKenney, etc.)

• Include regional data sets (??)

• Maps, Portrait Diagrams, Time Series, etc.

Comments, questions, ideas are all welcome!

Thank You!rbehnke@wisc.edu

Seasonal Maximum Temperature (119 Station Mean)

Seasonal Precipitation (119 Station Mean)

TMax  Data Set n FAC2 MB MGE NMB NMGE RMSE r COE

1 DayMet 10950 0.8431211 0.139863012.481726

0 0.010183252 0.18069138 3.46781220.959256

00.764378

9

2 JohnA 10957 0.7700100 -0.094658783.421430

2 -0.006894610 0.24920487 4.42983210.933569

00.675135

5

3 Livneh 10957 0.9726202 -0.069773340.339451

6 -0.005082043 0.02472445 0.58411260.998869

90.967769

1

4 Maurer 10957 0.9437751 -0.045391990.855736

1 -0.003306192 0.06232879 1.17045960.995389

40.918747

9

5 NLDAS2 10957 0.8381856 -1.074721931.913466

0 -0.078278941 0.13937009 2.42718720.985099

70.818316

6

6 TopoWx 10957 0.8758558 0.23122844 1.9141782

0.016841861 0.13942196 2.5840992 0.9774450

0.818248

A few of the many more statistics that can be used…