1. Moving from Empirical Estimation of Humidity to Observation: A Spatial and Temporal Evaluation of MTCLIM Assumptions Using Regional Networks
Ruben Behnke
Numerical Terradynamic Simulation Group
University of Montana
96th Annual AMS Meeting, New Orleans, LA
January 10 – 14, 2016

2. Gridded Climate Data DayMet PRISM Maurer Livneh CPC NLDAS2 UIdaho
At least 8 different high resolution, daily, gridded data sets for precipitation and temperature
DayMet
PRISM
Maurer
Livneh
CPC
NLDAS2
UIdaho
TopoWX

3. Measuring Humidity – A Very Brief History
MTCLIM Developed
Running, et al. (1987)
Running, S.W., Nemani, R.R., and Hungerford, R.D. (1987). Extrapolation of synoptic meteorological data in mountainous terrain and its use for simulating forest evapotranspiration and photosynthesis. Canadian Journal of Forest Research. Volume 17, 472 – 483.

4. Historical Lack of Station Data
Gaffen and Ross (1998) stations
(1961 – 1995)
Brown and DeGaetano (2012) stations
(1930 – 2010)
Brown PJ, DeGaetano AT Trends in U.S. Surface Humidity, J Appl Meteorol Climatol 52:
/1175/JAMC-D
Gaffen D, Ross R Climatology and trends of US surface humidity and temperature. J Clim 12: / (1999)012<0811:CATOUS>2.0.CO;2.
Robinson (1998)
221 stations
(1961 – 1990)
Robinson P Monthly variations of dew point temperature in the coterminous United States. Int J Climatol 18: /(SICI) ( )18:14<1539::AID-JOC326>3.0.CO;2-L.

5. Gridded Humidity Data MTCLIM (or similar) estimation Reanalysis Based
Livneh (Maurer) and DayMet data sets
VIC, RHESSYS, and other hydro-ecological models use this method
Three major assumptions
TMin = average daily dew point
Dew Point remains constant throughout the day
Humidity is spatially and temporally homogenous
Changes over short time and spatial scales are negligible
Reanalysis Based
NLDAS2 and UIdaho data sets
Humidity ‘strongly influenced’ by observations, but model determines final value

6. Project Goal – Use Station Data to Create Gridded Dew Point Data No longer estimate dew point from temperature, precipitation, day length, etc.
Mesonets have greatly expanded the amount of available humidity data
Short records aside, this data is very valuable and can be used to produce high resolution gridded humidity data sets
Quality Control is a major, necessary step in the process (even for MADIS data)
We are using this data to produce the first gridded humidity (average daily dew point) based directly on observations (1990 – 2015, daily, 800m)

7. Additional Mesonet Data Nearly triples Station Counts Short records of MOST stations => focus on spatial patterns
1948 – 2014
Use of MADIS (Meteorological Assimilation Data Ingestion System)
Downloaded data from about 30 networks that began before MADIS (2001)
12535 stations (with at least two full years of complete hourly data)

8. Relationship Between Vapor Pressure and Dew Point

9. Average Daily Dew Point
Relatively small N-S gradient in East U.S. in Summer
Mountain Ranges stand out
Strong gradient on west coast and California Central Valley

10. Average Daily Vapor Pressure
Non-linear relationship of dew point to vapor pressure leads to stronger N-S gradient in eastern U.S. during summer
Mountain ranges visible
Similar west coast patterns

11. Average Daily Minimum Relative Humidity
Very Strong E-W gradient in plains
Upper Midwest and NE shows high minimum RH during winter
Mountains not as clearly visible
West Coast gradients significantly more variable with season

12. Mean Daily Range – Vapor Pressure vs Dew Point
Vapor Pressure (Pa)
Dew Point (C)
In the arid west, a large change in dew point corresponds to a small change in vapor pressure
In the humid east, a small change in dew point corresponds to a large change in vapor pressure

13. Mean Daily Dew Point Mean Bias - DayMet
Significant underestimation along West Coast for Fall and Summer
Significant underestimation in Plains for spring
Overestimation in S. Rockies and Southwest in Fall
Overestimation in S. Plains in Summer
Mixed results elsewhere

14. Mean Daily Dew Point Mean Bias – NLDAS2
Biases not as extreme as DayMet
Overall tendency to overestimate, especially in NE U.S.
Stronger tendency to overestimate in winter, spring, and in high elevations
Stronger tendency to underestimate in Fall, Spring, and lower elevations

15. The Big Picture – Mean Daily Dew Point Biases in Gridded Products
DayMet and Livneh show very similar seasonal patterns in mean bias
UIdaho is biased too low, overall
NLDAS2 tends to be biased too high in all seasons, except for summer
NLDAS2 tends to be biased too low for all seasons in the Southeast

16. How well do current gridded products fare?
DayMet and Livneh model humidity according to MTCLIM (minimum temperature based)
NLDAS2 is downscaled 32 km NARR reanalysis data
UIdaho temporally disaggregates monthly PRISM data using NLDAS2
RTMA = Real Time Mesoscale Analysis (ingests observations from MADIS to produce forecast verification grids)

17. Seasonal Variations in Diurnal Dew Point Patterns

18. Questions and comments to: ruben.behnke@ntsg.umt.edu
Thank you!
Questions and comments to: