Thomas Schlatter

An Isentropic Three-Hourly Data Assimilation System Using ACARS Aircraft Observations

Abstract A 3-h intermittent data assimilation system (Mesoscale Analysis and Prediction System—MAPS) configured in isentropic coordinates was developed and implemented in real-time operation. The major components of the system are data ingest, objective quality control of the observation, objective analysis, and a primitive equation forecast model, all using isentropic coordinates to take advantage of the improved resolution near frontal zones and greater spatial coherence of data that this coordinate system provides. Each 3-h forecast becomes the background for the subsequent analysis; in this manner, a four-dimensional set of observations can be assimilated. The primary asynoptic data source used in current real-time operation of this system is air-craft data, most of it automated. Data from wind profilers, surface observations, and radiosondes are also included in MAPS. Statistics were collected over the last half of 1989 and into 1990 to study the performance of MAPS and compare it with that of the Re...

Research Opportunities from Emerging Atmospheric Observing and Modeling Capabilities

Abstract The Second Prospectus Development Team (PDT-2) of the U.S. Weather Research Program was charged with identifying research opportunities that are best matched to emerging operational and experimental measurement and modeling methods. The overarching recommendation of PDT-2 is that inputs for weather forecast models can best be obtained through the use of composite observing systems together with adaptive (or targeted) observing strategies employing both in situ and remote sensing. Optimal observing systems and strategies are best determined through a three-part process: observing system simulation experiments, pilot field measurement programs, and model-assisted data sensitivity experiments. Furthermore, the mesoscale research community needs easy and timely access to the new operational and research datasets in a form that can readily be reformatted into existing software packages for analysis and display. The value of these data is diminished to the extent that they remain inaccessible. The comp...

MULTIFUNCTIONAL MESOSCALE OBSERVING NETWORKS

Abstract More than 120 scientists, engineers, administrators, and users met on 8–10 December 2003 in a workshop format to discuss the needs for enhanced three-dimensional mesoscale observing networks. Improved networks are seen as being critical to advancing numerical and empirical modeling for a variety of mesoscale applications, including severe weather warnings and forecasts, hydrology, air-quality forecasting, chemical emergency response, transportation safety, energy management, and others. The participants shared a clear and common vision for the observing requirements: existing two-dimensional mesoscale measurement networks do not provide observations of the type, frequency, and density that are required to optimize mesoscale prediction and nowcasts. To be viable, mesoscale observing networks must serve multiple applications, and the public, private, and academic sectors must all actively participate in their design and implementation, as well as in the creation and delivery of value-added products...

Variational assimilation of meteorological observations in the lower atmosphere: A tutorial on how it works

Data assimilation combines atmospheric measurements with knowledge of atmospheric behavior as codified in computer models, thus producing a “best” estimate of current conditions that is consistent with both information sources. The four major challenges in data assimilation are: (1) to generate an initial state for a computer forecast that has the same mass-wind balance as the assimilating model, (2) to deal with the common problem of highly non-uniform distribution of observations, (3) to exploit the value of proxy observations (of parameters that are not carried explicitly in the model), and (4) to determine the statistical error properties of observing systems and numerical model alike so as to give each information source the proper weight. Variational data assimilation is practiced at major meteorological centers around the world. It is based upon multivariate linear regression, dating back to Gauss, and variational calculus. At the heart of the method is the minimization of a cost function, which guarantees that the analyzed fields will closely resemble both the background field (a short forecast containing a priori information about the atmospheric state) and current observations. The size of the errors in the background and the observations (the latter, arising from measurement and non-representativeness) determine how close the analysis is to each basic source of information. Three-dimensional variational (3DVAR) assimilation provides a logical framework for incorporating the error information (in the form of variances and spatial covariances) and deals directly with the problem of proxy observations. 4DVAR assimilation is an extension of 3DVAR assimilation that includes the time dimension; it attempts to find an evolution of model states that most closely matches observations taken over a time interval measured in hours. Both 3DVAR and, especially, 4DVAR assimilation require very large computing resources. Researchers are trying to find more efficient numerical solutions to these problems. Variational assimilation is applicable in the upper atmosphere, but practical implementation demands accurate modeling of the physical processes that occur at high altitudes and multiple sources of observations.

Statistical Properties of Three-Hour Prediction “Errors” Derived from the Mesoscale Analysis and Prediction System

Abstract Statistical properties of observed residuals from the Mesoscale Analysis and Prediction System (MAPS), a real-time data assimilation system, were investigated. Observed residuals are defined as differences between rawinsonde observations interpolated vertically to the model levels and the predicted values from MAPS interpolated horizontally to the radiosonde locations. One-point statistical moments up to order 4 (including skewness and flatness) were computed to investigate the normality of the probability distribution of observed residuals. The finding of near-zero skewness indicates symmetry in the distribution of observed residuals, but values of flatness significantly different from 3 indicate deviations from a normal (Gaussian) distribution. These results are supported by an effective statistical test. The spatial distributions of these statistical moments show strong local variability, which is ascribed to occasional gross errors in the rawinsonde data. The spatial correlation of observed r...

An Unusual Hailstorm on 24 June 2006 in Boulder, Colorado. Part I: Mesoscale Setting and Radar Features

Abstract An unusual, isolated hailstorm descended on Boulder, Colorado, on the evening of 24 June 2006. Starting with scattered large, flattened, disk-shaped hailstones and ending with a deluge of slushy hail that was over 4 cm deep on the ground, the storm lasted no more than 20 min and did surprisingly little damage except to vegetation. Part I of this two-part paper examines the meteorological conditions preceding the storm and the signatures it exhibited on Weather Surveillance Radar-1988 Doppler (WSR-88D) displays. There was no obvious upper-tropospheric forcing for this storm, vertical shear of the low-level wind was minimal, the boundary layer air feeding the storm was not very moist (maximum dewpoint 8.5°C), and convective available potential energy calculated from a modified air parcel was at most 1550 J kg−1. Despite these handicaps, the hail-producing storm had low-level reflectivity exceeding 70 dBZ, produced copious low-density hail, exhibited strong rotation, and generated three extensive bo...

An Unusual Hailstorm on 24 June 2006 in Boulder, Colorado. Part II: Low-Density Growth of Hail

The 24 June 2006 Boulder hailstorm produced very heavy precipitation including disklike hailstones that grew with low density. These disklike hailstones, 4–5 cm in diameter, are unusual, and some of them appear to have accumulated graupel while aloft. A large amount of very fine-grained slush was left on the ground along with the hail. The hail and the great amount of slush suggest that most of the hydrometeor growth in the cloud was by low- or very-low-density riming. Consistent with that, the radar data suggest that the storm updraft had substantially depleted liquid water content. There is evidence that low-density hydrometeor growth within storms may be considerably more frequent than is commonly suspected.

A Striking Cloud Over Boulder, Colorado: What Is Its Altitude, and Why Does It Matter?

Scientific investigation is supposed to be objective and strictly logical, but this is not always the case: the process that leads to a good conclusion can be messy. This narrative describes interactions among a group of scientists trying to solve a simple problem that had scientific implications. It started with the observation of a cloud exhibiting behavior associated with supercooled water and temperatures around −20°C. However, other aspects of the cloud suggested an altitude where the temperature was around −40°C. For several months following the appearance of the cloud on 23 March 2011, the people involved searched for evidence, formed strong opinions, argued, examined evidence more carefully, changed their minds, and searched for more evidence until they could reach agreement. While they concluded that the cloud was at the higher and colder altitude, evidence for supercooled liquid water at that altitude is not conclusive.

Weather Queries: “Shelf” and “Roll” Clouds

(1984). Weather Queries: “Shelf” and “Roll” Clouds. Weatherwise: Vol. 37, No. 4, pp. 208-209.