Search Results
You are looking at 1 - 6 of 6 items for
- Author or Editor: Daniel P. Tyndall x
- Refine by Access: All Content x
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.
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.
Assimilating Near-Surface Wind Retrievals from High-Frequency RadarsAbstract
High-frequency radars (HFR) are traditionally used in coastal environments to observe ocean current and wave characteristics. With an HFR forward model, HFR adjoint model, and the Simulating Waves Nearshore model, HFR Doppler spectra observations were used to estimate near-surface winds in the Southern California Bight in October 2017. The HFR 10-m wind retrievals were assimilated into the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) with the COAMPS four-dimensional variational (4DVar) assimilation system to integrate the HFR wind retrievals into the initial conditions. Impact of the HFR-derived winds on the forecast are evaluated in terms of adjoint-derived forecast sensitivity observation impact (FSOI), and by an observing system experiment that compared forecasts from simulations that assimilated the HFR wind retrievals to a control simulation that excluded HFR winds. The addition of the HFR-estimated wind observations reduced the error in the forecasted dry energy norm in the lowest model level and also contributed to small improvements in the 10-m wind field over a 25-day experiment. The potential benefit of this new method to estimate near-surface ocean winds near the coast for data assimilation and improved numerical weather prediction is an exciting advancement in remote sensing of coastal winds and expands the benefit of existing HFR networks beyond their intended use. More importantly, wind fields retrieved from HFR have the potential to fill an observation gap near the shoreline where ship and buoy observations are sparse and scatterometer observations are unavailable due to land contamination.
Abstract
High-frequency radars (HFR) are traditionally used in coastal environments to observe ocean current and wave characteristics. With an HFR forward model, HFR adjoint model, and the Simulating Waves Nearshore model, HFR Doppler spectra observations were used to estimate near-surface winds in the Southern California Bight in October 2017. The HFR 10-m wind retrievals were assimilated into the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) with the COAMPS four-dimensional variational (4DVar) assimilation system to integrate the HFR wind retrievals into the initial conditions. Impact of the HFR-derived winds on the forecast are evaluated in terms of adjoint-derived forecast sensitivity observation impact (FSOI), and by an observing system experiment that compared forecasts from simulations that assimilated the HFR wind retrievals to a control simulation that excluded HFR winds. The addition of the HFR-estimated wind observations reduced the error in the forecasted dry energy norm in the lowest model level and also contributed to small improvements in the 10-m wind field over a 25-day experiment. The potential benefit of this new method to estimate near-surface ocean winds near the coast for data assimilation and improved numerical weather prediction is an exciting advancement in remote sensing of coastal winds and expands the benefit of existing HFR networks beyond their intended use. More importantly, wind fields retrieved from HFR have the potential to fill an observation gap near the shoreline where ship and buoy observations are sparse and scatterometer observations are unavailable due to land contamination.
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.
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.
Abstract
The Trident Warrior observational field campaign conducted off the U.S. mid-Atlantic coast in July 2013 included the deployment of an unmanned aerial system (UAS) with several payloads on board for atmospheric and oceanic observation. These UAS observations, spanning seven flights over 5 days in the lowest 1550 m above mean sea level, were assimilated into a three-dimensional variational data assimilation (DA) system [the Naval Research Laboratory Atmospheric Variational Data Assimilation System (NAVDAS)] used to generate analyses for a numerical weather prediction model [the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS)] with a coupled ocean model [the Naval Research Laboratory Navy Coastal Ocean Model (NCOM)]. The impact of the assimilated UAS observations on short-term atmospheric prediction performance is evaluated and quantified. Observations collected from 50 radiosonde launches during the campaign adjacent to the UAS flight paths serve as model forecast verification. Experiments reveal a substantial reduction of model bias in forecast temperature and moisture profiles consistently throughout the campaign period due to the assimilation of UAS observations. The model error reduction is most substantial in the vicinity of the inversion at the top of the model-estimated boundary layer. Investigations reveal a consistent improvement to prediction of the vertical position, strength, and depth of the boundary layer inversion. The relative impact of UAS observations is explored further with experiments of systematic denial of data streams from the NAVDAS DA system and removal of individual measurement sources on the UAS platform.
Abstract
The Trident Warrior observational field campaign conducted off the U.S. mid-Atlantic coast in July 2013 included the deployment of an unmanned aerial system (UAS) with several payloads on board for atmospheric and oceanic observation. These UAS observations, spanning seven flights over 5 days in the lowest 1550 m above mean sea level, were assimilated into a three-dimensional variational data assimilation (DA) system [the Naval Research Laboratory Atmospheric Variational Data Assimilation System (NAVDAS)] used to generate analyses for a numerical weather prediction model [the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS)] with a coupled ocean model [the Naval Research Laboratory Navy Coastal Ocean Model (NCOM)]. The impact of the assimilated UAS observations on short-term atmospheric prediction performance is evaluated and quantified. Observations collected from 50 radiosonde launches during the campaign adjacent to the UAS flight paths serve as model forecast verification. Experiments reveal a substantial reduction of model bias in forecast temperature and moisture profiles consistently throughout the campaign period due to the assimilation of UAS observations. The model error reduction is most substantial in the vicinity of the inversion at the top of the model-estimated boundary layer. Investigations reveal a consistent improvement to prediction of the vertical position, strength, and depth of the boundary layer inversion. The relative impact of UAS observations is explored further with experiments of systematic denial of data streams from the NAVDAS DA system and removal of individual measurement sources on the UAS platform.
Abstract
The NOAA Science Advisory Board appointed a task force to prepare a white paper on the use of observing system simulation experiments (OSSEs). Considering the importance and timeliness of this topic and based on this white paper, here we briefly review the use of OSSEs in the United States, discuss their values and limitations, and develop five recommendations for moving forward: national coordination of relevant research efforts, acceleration of OSSE development for Earth system models, consideration of the potential impact on OSSEs of deficiencies in the current data assimilation and prediction system, innovative and new applications of OSSEs, and extension of OSSEs to societal impacts. OSSEs can be complemented by calculations of forecast sensitivity to observations, which simultaneously evaluate the impact of different observation types in a forecast model system.
Abstract
The NOAA Science Advisory Board appointed a task force to prepare a white paper on the use of observing system simulation experiments (OSSEs). Considering the importance and timeliness of this topic and based on this white paper, here we briefly review the use of OSSEs in the United States, discuss their values and limitations, and develop five recommendations for moving forward: national coordination of relevant research efforts, acceleration of OSSE development for Earth system models, consideration of the potential impact on OSSEs of deficiencies in the current data assimilation and prediction system, innovative and new applications of OSSEs, and extension of OSSEs to societal impacts. OSSEs can be complemented by calculations of forecast sensitivity to observations, which simultaneously evaluate the impact of different observation types in a forecast model system.
