Ryan D. Torn

The Data Assimilation Research Testbed: A Community Facility

Abstract The Data Assimilation Research Testbed (DART) is an open-source community facility for data assimilation education, research, and development. DARTs ensemble data assimilation algorithms, careful software engineering, and diagnostic tools allow atmospheric scientists, oceanographers, hydrologists, chemists, and other geophysicists to build state-of-the-art data assimilation systems with unprecedented ease. For global numerical weather prediction, DART produces ensemble-mean analyses comparable to analyses from major centers while also providing initial conditions for ensemble predictions. In addition, DART supports more novel assimilation applications like parameter estimation, sensitivity analysis, observing system design, and smoothing. Implementing basic systems for large models requires only a few person-weeks; comprehensive systems have been built in a few months. Incorporating new observation types is also straightforward, requiring only a forward operator mapping between a models state a...

Boundary Conditions for Limited-Area Ensemble Kalman Filters

Abstract One aspect of implementing a limited-area ensemble Kalman filter (EnKF) involves the specification of a suitable ensemble of lateral boundary conditions. Two classes of methods to populate a boundary condition ensemble are proposed. In the first class, the ensemble of boundary conditions is provided by an EnKF on a larger domain and is approximately a random draw from the probability distribution function for the forecast (or analysis) on the limited-area domain boundary. The second class perturbs around a deterministic estimate of the state using assumed spatial and temporal covariance relationships. Methods in the second class are relatively flexible and easy to implement. Experiments that test the utility of these methods are performed for both an idealized low-dimensional model and limited-area simulations using the Weather Research and Forecasting (WRF) model; all experiments employ simulated observations under the perfect model assumption. The performance of the ensemble boundary condition ...

A Multiscale Remote Sensing Model for Disaggregating Regional Fluxes to Micrometeorological Scales

Abstract Disaggregation of regional-scale (103 m) flux estimates to micrometeorological scales (101–102 m) facilitates direct comparison between land surface models and ground-based observations. Inversely, it also provides a means for upscaling flux-tower information into a regional context. The utility of the Atmosphere–Land Exchange Inverse (ALEXI) model and associated disaggregation technique (DisALEXI) in effecting regional to local downscaling is demonstrated in an application to thermal imagery collected with the Geostationary Operational Environmental Satellite (GOES) (5-km resolution) and Landsat (60-m resolution) over the state of Oklahoma on 4 days during 2000–01. A related algorithm (DisTrad) sharpens thermal imagery to resolutions associated with visible–near-infrared bands (30 m on Landsat), extending the range in scales achievable through disaggregation. The accuracy and utility of this combined multiscale modeling system is evaluated quantitatively in comparison with measurements made with...

The Pre-Depression Investigation of Cloud-Systems in the Tropics (PREDICT) Experiment: Scientific Basis, New Analysis Tools, and Some First Results

The principal hypotheses of a new model of tropical cyclogenesis, known as the marsupial paradigm, were tested in the context of Atlantic tropical disturbances during the National Science Foundation (NSF)-sponsored Pre-Depression Investigation of Cloud Systems in the Tropics (PREDICT) experiment in 2010. PREDICT was part of a tri-agency collaboration, along with the National Aeronautics and Space Administrations Genesis and Rapid Intensification Processes (NASA GRIP) experiment and the National Oceanic and Atmospheric Administrations Intensity Forecasting Experiment (NOAA IFEX), intended to examine both developing and nondeveloping tropical disturbances. During PREDICT, a total of 26 missions were flown with the NSF/NCAR Gulfstream V (GV) aircraft sampling eight tropical disturbances. Among these were four cases (Fiona, ex-Gaston, Karl, and Matthew) for which three or more missions were conducted, many on consecutive days. Because of the scientific focus on the Lagrangian nature of the tropical cyclogen...

Estimating Land Surface Energy Budgets From Space: Review and Current Efforts at the University of Wisconsin—Madison and USDA–ARS

Abstract Since the advent of the meteorological satellite, a large research effort within the community of earth scientists has been directed at assessing the components of the land surface energy balance from space. The development of these techniques from first efforts to the present time are reviewed, and the integrated system used to estimate the radiative and turbulent land surface fluxes is described. This system is now running in real time over the continental United States at a resolution of 10 km, producing daily and time-integrated flux components.

Ensemble-Based Sensitivity Analysis

The sensitivity of forecasts to observations is evaluated using an ensemble approach with data drawn from a pseudo-operational ensemble Kalman filter. For Gaussian statistics and a forecast metric defined as a scalar function of the forecast variables, the effect of observations on the forecast metric is quantified by changes in the metric mean and variance. For a single observation, expressions for these changes involve a product of scalar quantities, which can be rapidly evaluated for large numbers of observations. This technique is applied to determining climatological forecast sensitivity and predicting the impact of observations on sea level pressure and precipitation forecast metrics. The climatological 24-h forecast sensitivity of the average pressure over western Washington State shows a region of maximum sensitivity to the west of the region, which tilts gently westward with height. The accuracy of ensemble sensitivity predictions is tested by withholding a single buoy pressure observation from this region and comparing this perturbed forecast with the control case where the buoy is assimilated. For 30 cases, there is excellent agreement between these forecast differences and the ensemble predictions, as measured by the forecast metric. This agreement decreases for increasing numbers of observations. Nevertheless, by using statistical confidence tests to address sampling error, the impact of thousands of observations on forecast-metric variance is shown to be well estimated by a subset of the O(100) most significant observations.

Performance of a Mesoscale Ensemble Kalman Filter (EnKF) during the NOAA High-Resolution Hurricane Test

Abstract An ensemble Kalman filter (EnKF) combined with the Advanced Research Weather Research and Forecasting model (ARW-WRF; hereafter WRF) on a 36-km Atlantic basin domain is cycled over six different time periods that include the 10 tropical cyclones (TCs) selected for the NOAA High-Resolution Hurricane (HRH) test. The analysis ensemble is generated every 6 h by assimilating conventional in situ observations, synoptic dropsondes, and TC advisory position and minimum sea level pressure (SLP) data. On average, observation assimilation leads to smaller TC position errors in the analysis compared to the 6-h forecast; however, the same is true for TC minimum SLP only for tropical depressions and storms. Over the 69 HRH initialization times, TC track forecasts from a single member of the WRF EnKF ensemble has 12 h less skill compared to other operational models; the increased track error partially results from the WRF EnKF analysis having a stronger Atlantic subtropical ridge. For nonmajor TCs, the WRF EnKF...

Performance Characteristics of a Pseudo-Operational Ensemble Kalman Filter

The 2-yr performance of a pseudo-operational (real time) limited-area ensemble Kalman filter (EnKF) based on the Weather Research and Forecasting Model is described. This system assimilates conventional observations from surface stations, rawinsondes, the Aircraft Communications Addressing and Reporting System (ACARS), and cloud motion vectors every 6 h on a domain that includes the eastern North Pacific Ocean and western North America. Ensemble forecasts from this system and deterministic output from operational numerical weather prediction models during this same period are verified against rawinsonde and surface observation data. Relative to operational forecasts, the forecast from the ensemble-mean analysis has slightly larger errors in wind and temperature but smaller errors in moisture, even though satellite radiances are not assimilated by the EnKF. Time-averaged correlations indicate that assimilating ACARS and cloud wind data with flow-dependent error statistics provides corrections to the moisture field in the absence of direct observations of that field. Comparison with a control experiment in which a deterministic forecast is cycled without observation assimilation indicates that the skill in the EnKF’s forecasts results from assimilating observations and not from lateral boundary conditions or the model formulation. Furthermore, the ensemble variance is generally in good agreement with the ensemble-mean error and the spread increases monotonically with forecast hour.

Does Increased Horizontal Resolution Improve Hurricane Wind Forecasts

Abstract The representation of tropical cyclone track, intensity, and structure in a set of 69 parallel forecasts performed at each of two horizontal grid increments with the Advanced Research Hurricane (AHW) component of the Weather and Research and Forecasting Model (WRF) is evaluated. These forecasts covered 10 Atlantic tropical cyclones: 6 from the 2005 season and 4 from 2007. The forecasts were integrated from identical initial conditions produced by a cycling ensemble Kalman filter. The high-resolution forecasts used moving, storm-centered nests of 4- and 1.33-km grid spacing. The coarse-resolution forecasts consisted of a single 12-km domain (which was identical to the outer domain in the forecasts with nests). Forecasts were evaluated out to 120 h. Novel verification techniques were developed to evaluate forecasts of wind radii and the degree of storm asymmetry. Intensity (maximum wind) and rapid intensification, as well as wind radii, were all predicted more accurately with increased horizontal r...

Uncertainty of Tropical Cyclone Best-Track Information

With the growing use of tropical cyclone (TC) best-track information for weather and climate applications, it isimportanttounderstandthe uncertaintiesthatare containedin the TCpositionandintensityinformation. Here, an attempt is made to quantify the position uncertainty using National Hurricane Center (NHC) advisory information, as well as intensity uncertainty during times without aircraft data, by verifying Dvorak minimum sea level pressure (SLP) and maximum wind speed estimates during times with aircraft reconnaissanceinformationduring2000‐09.Inaclimatologicalsense,TCpositionuncertaintydecreasesformore intense TCs, while the uncertainty of intensity, measured by minimum SLP or maximum wind speed, increases with intensity. The standard deviation of satellite-based TC intensity estimates can be used as a predictor of the consensus intensity error when that consensus includes both Dvorak and microwave-based estimates, but not when it contains only Dvorak-based values. Whereas there has been a steady decrease in seasonal TC position uncertainty overthe past 10 yr, whichis likely due toadditionaldata available toNHC forecasters, the seasonal TC minimum SLP and maximum wind speed values are fairly constant, with year-to-year variability due to the mean intensity of all TCs during that season and the frequency of aircraft reconnaissance.