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

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

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.

Historical Lack of Station Data Gaffen and Ross (1998) 188 stations (1961 – 1995) Brown and DeGaetano (2012) 145 stations (1930 – 2010) Brown PJ, DeGaetano AT. 2013. Trends in U.S. Surface Humidity, 1930-2010. J Appl Meteorol Climatol 52: 147-163. 10/1175/JAMC-D-12-035.1. Gaffen D, Ross R. 1999. Climatology and trends of US surface humidity and temperature. J Clim 12: 811-828. 10.1175/1520-0442(1999)012<0811:CATOUS>2.0.CO;2. Robinson (1998) 221 stations (1961 – 1990) Robinson P. 1998. Monthly variations of dew point temperature in the coterminous United States. Int J Climatol 18: 1539-1556. 10.1002/(SICI)1097-0088(19981130)18:14<1539::AID-JOC326>3.0.CO;2-L.

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

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)

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)

Relationship Between Vapor Pressure and Dew Point

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

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

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

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

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

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

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

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)

Seasonal Variations in Diurnal Dew Point Patterns

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