John Kochendorfer

How Well Are We Measuring Snow: The NOAA/FAA/NCAR Winter Precipitation Test Bed

This paper presents recent efforts to understand the relative accuracies of different instrumentation and gauges with various windshield configurations to measure snowfall. Results from the National Center for Atmospheric Research (NCAR) Marshall Field Site will be highlighted. This site hosts a test bed to assess various solid precipitation measurement techniques and is a joint collaboration between the National Oceanic and Atmospheric Administration (NOAA), NCAR, the National Weather Service (NWS), and Federal Aviation Administration (FAA). The collaboration involves testing new gauges and other solid precipitation measurement techniques in comparison with World Meteorological Organization (WMO) reference snowfall measurements. This assessment is critical for any ongoing studies and applications, such as climate monitoring and aircraft deicing, that rely on accurate and consistent precipitation measurements.

U.S. Climate Reference Network Soil Moisture and Temperature Observations

AbstractThe U.S. Climate Reference Network (USCRN) is a network of climate-monitoring stations maintained and operated by the National Oceanic and Atmospheric Administration (NOAA) to provide climate-science-quality measurements of air temperature and precipitation. The stations in the network were designed to be extensible to other missions, and the National Integrated Drought Information System program determined that the USCRN could be augmented to provide observations that are more drought relevant. To increase the network’s capability of monitoring soil processes and drought, soil observations were added to USCRN instrumentation. In 2011, the USCRN team completed at each USCRN station in the conterminous United States the installation of triplicate-configuration soil moisture and soil temperature probes at five standards depths (5, 10, 20, 50, and 100 cm) as prescribed by the World Meteorological Organization; in addition, the project included the installation of a relative humidity sensor at each of...

An Improved Trajectory Model to Evaluate the Collection Performance of Snow Gauges

Recent studies have used numerical models to estimate the collection efficiency of solid precipitation gauges when exposed to the wind in both shielded and unshielded configurations. The models used computational fluid dynamics (CFD) simulations of the airflow pattern generated by the aerodynamic response to the gauge‐shield geometry. These are used as initial conditions to perform Lagrangian tracking of solid precipitation particles. Validation of the results against field observations yielded similarities in the overall behavior, but the model output only approximately reproduced the dependence of the experimental collection efficiency on wind speed. This paper presents an improved snowflake trajectory modeling scheme due to the inclusion of a dynamically determined drag coefficient. The drag coefficient was estimated using the local Reynolds number as derived from CFD simulations within a time-independent Reynoldsaveraged Navier‐Stokes approach. The proposed dynamic model greatly improves the consistency of results with the field observations recently obtained at the Marshall Field winter precipitation test bed in Boulder, Colorado.

Measurement of trace gas fluxes over an unfertilized agricultural field using the flux-gradient technique.

Trace gas fluxes exhibit extensive spatial and temporal variability that is dependent on a number of factors, including meteorology, ambient concentration, and emission source size. Previous studies have found that agricultural fertilization contributes to higher fluxes of certain gases. The magnitude of trace gas fluxes over unfertilized crops is still uncertain. In the present study, deposition of ammonia (NH), nitric acid (HNO), and sulfur dioxide (SO) was measured over unfertilized soybean using the flux-gradient technique. The eddy diffusivity was estimated from eddy covariance measurements of temperature fluxes, resulting in K of 0.64 ± 0.30 m s. Flux means and standard deviations were -0.14 ± 0.13, -0.22 ± 0.19, and -0.38 ± 0.54 μg m s for NH, HNO, and SO, respectively. Low concentrations of NH and HNO increased the relative uncertainties in the deposition velocities estimated from measured fluxes. This contributed to dissimilarities between deposition velocities estimated from the resistance analogy and deposition velocities estimated from fluxes. However, wet canopy conditions during the study may have led to an underestimation of deposition by the resistance analogy because the resistance method does not accurately describe the enhanced deposition rates that occur after dew formation. Quantification of vegetation characteristics, such as leaf wetness and apoplast chemistry, would be beneficial in future studies to more accurately determine stomatal resistance and its influence on fluxes.

Estimating Random Uncertainty in Airborne Flux Measurements over Alaskan Tundra: Update on the Flux Fragment Method

AbstractAirborne turbulence measurement gives a spatial distribution of air–surface fluxes that networks of fixed surface sites typically cannot capture. Much work has improved the accuracy of such measurements and the estimation of the uncertainty peculiar to streams of turbulence data measured from the air. A particularly significant challenge and opportunity is to distinguish fluxes from different surface types, especially those occurring in patches smaller than the necessary averaging length. The flux fragment method (FFM), a conditional-sampling variant of eddy covariance in the space–time domain, was presented in 2008. It was shown capable of segregating the mean flux density (CO2, H2O, sensible heat) in maize from that in soybeans over the patchwork farmlands of Illinois. This was, however, an ideal surface for the method, and the random-error estimate used a relatively rudimentary bootstrap resampling. The present paper describes an upgraded random-error estimate that accounts for the serial corre...