Search Results

You are looking at 1 - 2 of 2 items for :

  • Author or Editor: Daniel P. Tyndall x
  • Weather and Forecasting x
  • Refine by Access: Content accessible to me x
Clear All Modify Search
Daniel P. Tyndall
and
John D. Horel

Abstract

Given the heterogeneous equipment, maintenance and reporting practices, and siting of surface observing stations, subjective decisions that depend on the application tend to be made to use some observations and to avoid others. This research determines objectively high-impact surface observations of 2-m temperature, 2-m dewpoint, and 10-m wind observations using the adjoint of a two-dimensional variational surface analysis over the contiguous United States. The analyses reflect a weighted blend of 1-h numerical forecasts used as background grids and available observations. High-impact observations are defined as arising from poor observation quality, observation representativeness errors, or accurate observed weather conditions not evident in the background field. The impact of nearly 20 000 surface observations is computed over a sample of 100 analysis hours during 25 major weather events. Observation impacts are determined for each station as well as within broad network categories. For individual analysis hours, high-impact observations are located in regions of significant weather—typically, where the background field fails to define the local weather conditions. Low-impact observations tend to be ones where there are many observations reporting similar departures from the background. When averaged over the entire 100 cases, observations with the highest impact are found within all network categories and depend strongly on their location relative to other observing sites and the amount of variability in the weather; for example, temperature observations have reduced impact in urban areas such as Los Angeles, California, where observations are plentiful and temperature departures from the background grids are small.

Full access
Daniel P. Tyndall
,
John D. Horel
, and
Manuel S. F. V. de Pondeca

Abstract

A two-dimensional variational method is used to analyze 2-m air temperatures over a limited domain (4° latitude × 4° longitude) in order to evaluate approaches to examining the sensitivity of the temperature analysis to the specification of observation and background errors. This local surface analysis (LSA) utilizes the 1-h forecast from the Rapid Update Cycle (RUC) downscaled to a 5-km resolution terrain level for its background fields and observations obtained from the Meteorological Assimilation Data Ingest System.

The observation error variance as a function of broad network categories and the error variance and covariance of the downscaled 1-h RUC background fields are estimated using a sample of over 7 million 2-m air temperature observations in the continental United States collected during the period 8 May–7 June 2008. The ratio of observation to background error variance is found to be between 2 and 3. This ratio is likely even higher in mountainous regions where representativeness errors attributed to the observations are large.

The technique used to evaluate the sensitivity of the 2-m air temperature to the ratio of the observation and background error variance and background error length scales is illustrated over the Shenandoah Valley of Virginia for a particularly challenging case (0900 UTC 22 October 2007) when large horizontal temperature gradients were present in the mountainous regions as well as over two entire days (20 and 27 May 2009). Sets of data denial experiments in which observations are randomly and uniquely removed from each analysis are generated and evaluated. This method demonstrates the effects of overfitting the analysis to the observations.

Full access