Bradley Zavodsky

The use of error estimates with AIRS profiles to improve short-term weather forecasts

The hyperspectral resolution measurements from the NASA Atmospheric Infrared Sounder (AIRS) are advancing climate research by mapping atmospheric temperature, moisture, and trace gases on a global basis with unprecedented accuracy. Using a sophisticated retrieval scheme, the AIRS is capable of diagnosing the atmospheric temperature in the troposphere with accuracies of less than 1 K over 1 km-thick layers and 10-20% relative humidity over 2 km-thick layers, under both clear and cloudy conditions. A unique aspect of the retrieval procedure is the specification of a vertically varying error estimate for the temperature and moisture profile for each retrieval. The error specification allows for the more selective use of the profiles in subsequent processing. In this paper, we describe a procedure to assimilate AIRS data into the Weather Research and Forecasting (WRF) model to improve short-term weather forecasts. The ARPS Data Analysis System (ADAS) developed by the University of Oklahoma is configured to optimally blend AIRS data with model background fields based on the AIRS error profiles. The WRF short-term forecasts with selected AIRS data show improvement over the control forecast. The use of the AIRS error profiles maximizes the impact of high quality AIRS data from portions of the profile in the assimilation/forecast process without degradation from lower quality data in the other portions of the profile.

Evaluation of Data Reduction Algorithms for Real-Time Analysis

Abstract Data reduction tools are developed and evaluated using a data analysis framework. Simple (nonadaptive) and intelligent (adaptive) thinning algorithms are applied to both synthetic and real data and the thinned datasets are ingested into an analysis system. The approach is motivated by the desire to better represent high-impact weather features (e.g., fronts, jets, cyclones, etc.) that are often poorly resolved in coarse-resolution forecast models and to efficiently generate a set of initial conditions that best describes the current state of the atmosphere. As a precursor to real-data applications, the algorithms are applied to one- and two-dimensional synthetic datasets. Information gleaned from the synthetic experiments is used to create a thinning algorithm that combines the best aspects of the intelligent methods (i.e., their ability to detect regions of interest) while reducing the impacts of spatial irregularities in the data. Both simple and intelligent thinning algorithms are then applied...

Using Sensor Web Processes and Protocols to Assimilate Satellite Data into a Forecast Model

Working closely with atmospheric scientists at the Marshall Space Flight Center, researchers at the University of Alabama in Huntsville are applying Sensor Web Enablement (SWE) technologies to the real world problem of efficiently assimilating NASA satellite data into weather forecast models in near real time. By implementing SWE protocols and services into our Data Assimilation System we expect to realize a processing framework that is distributed, interoperable and plug-and-play, thereby increasing access to scientific products in a more efficient, autonomous, and affordable way.

Intelligent Data Thinning Algorithms for Satellite Imagery

This paper presents a study on intelligent data thinning for satellite data. In particular, the focus is on the thinning of the Atmospheric Infrared Sounder (AIRS) profiles. A direct thinning method is first applied to a synthetic data set in order to identify optimal data selection strategies. Experiments on synthetic data suggest that a thinned data set should combine homogeneous samples, and high gradient and variance of gradient samples for optimal performance. This result leads to the modification of our previously developed Density Adjustment Data Thinning algorithm (DADT). The modified DADT (mDADT) algorithm is used to thin the AIRS profiles. Experiments are conducted to compare the thinning performances of mDADT with two simple thinning algorithms. Experiment results show that mDADT algorithm performs better than the two simple thinning algorithms, especially over the regions of significant atmospheric features.