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Daniel Birkenheuer

Abstract

Horizontal shape matching (HSM) is a variational technique used to obtain an analysis that merges the gradient structure from one data source (e.g., satellite) with a background field. This technique is modeled after the variational methods that fall into two general categories: strong constraint and weak constraint. HSM, which is essentially a specialized filter function, falls in the weak constraint category. Here a technique is demonstrated that effectively tunes the filter to the spatial resolution inherent in the data sources rather than on the absolute error of the data.

The HSM equation is developed for satellite gradient insertion including a cloud treatment. The filtering properties of the HSM equation are explored using Fourier analysis, and an approximate numerical solution is shown to be viable for operational use. Applying this technique to a dataset spanning more than a year proves the analysis has a positive effect through the statistical significance of a large sample of comparisons with radiosonde observation data.

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Daniel Birkenheuer

Abstract

An algorithm for the operational integration of satellite-derived and ground-based passive microwave precipitable-water measurements is presented. The technique merges the two data types by relying on the more. accurate ground-based data to correct the bias and scale the satellite field, thus providing an improved meso-β-scale analysis. The complete algorithm is described from the preparation of the derived satellite imagery, used here as a data source, to the optimization routine used to integrate the data. The technique is now used for a local-scale analysis of the Denver area. One case is presented illustrating the effectiveness of the technique to track a weak moisture gradient through eastern Colorado. Other cases demonstrate the performance of the analysis in varying topography.

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Daniel Birkenheuer

Abstract

The Local Analysis and Prediction System (LAPS) analyzes three-dimensional moisture as one component of its system. This paper describes the positive impact that simple 8-bit, remapped, routinely available imagery have on the LAPS moisture analysis above 500 hPa. A variational method adjusts the LAPS moisture analysis by minimizing differences between forward model-computed radiances and radiances from Advanced Weather Interactive Processing System (AWIPS) image-grade data from Geostationary Operational Environmental Satellite 8 (GOES-8). The three infrared channels used in the analysis will be routinely available to AWIPS workstations every 15 min. This technique improves LAPS upper-level dewpoint, reducing dewpoint temperature bias and root-mean-square error on the order of 0.5 and 1.5 K, respectively, as compared to Denver radiosonde observation data. Furthermore, it strongly exemplifies the objective analysis benefit of image-grade data, in addition to its well-known subjective utility.

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Daniel Birkenheuer and Seth Gutman

Abstract

Geostationary Operational Environmental Satellite (GOES) sounder–derived total column water vapor is compared with other data sources obtained during the 2002 International H2O Project (IHOP-2002) field experiment. Specifically, GPS-derived total integrated precipitable water (GPS-IPW) and radiosonde observations (raob) data are used to assess GOES bias and standard deviation. GPS integrated water calculated from signal delay closely matches raob data, both from special sondes launched for the IHOP-2002 exercise and routine National Weather Service (NWS) soundings. After examining the average differences between GPS and GOES product total precipitable water over the full diurnal cycle between 26 May and 15 June 2002, it was discovered that only 0000 UTC time differences were comparable to published comparisons. Differences at other times were larger and varied by a factor of 6, increasing from 0000 to 1800 UTC, and decreasing thereafter. Reasons for this behavior are explored to a limited degree but with no clear answers to explain the observations. It is concluded that a component of the GOES total precipitable water error (between sonde launches) might be missed when solely assessing the data against synoptic raobs.

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Glen E. Liston, Daniel L. Birkenheuer, Christopher A. Hiemstra, Donald W. Cline, and Kelly Elder

Abstract

This paper describes the Local Analysis and Prediction System (LAPS) and the 20-km horizontal grid version of the Rapid Update Cycle (RUC20) atmospheric analyses datasets, which are available as part of the Cold Land Processes Field Experiment (CLPX) data archive. The LAPS dataset contains spatially and temporally continuous atmospheric and surface variables over Colorado, Wyoming, and parts of the surrounding states. The analysis used a 10-km horizontal grid with 21 vertical levels and an hourly temporal resolution. The LAPS archive includes forty-six 1D surface fields and nine 3D upper-air fields, spanning the period 1 September 2001 through 31 August 2003. The RUC20 dataset includes hourly 3D atmospheric analyses over the contiguous United States and parts of southern Canada and northern Mexico, with 50 vertical levels. The RUC20 archive contains forty-six 1D surface fields and fourteen 3D upper-air fields, spanning the period 1 October 2002 through 31 September 2003. The datasets are archived at the National Snow and Ice Data Center (NSIDC) in Boulder, Colorado.

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Christopher A. Hiemstra, Glen E. Liston, Roger A. Pielke Sr., Daniel L. Birkenheuer, and Steven C. Albers

Abstract

Meteorological forcing data are necessary to drive many of the spatial models used to simulate atmospheric, biological, and hydrological processes. Unfortunately, many domains lack sufficient meteorological data and available point observations are not always suitable or reliable for landscape or regional applications. NOAA’s Local Analysis and Prediction System (LAPS) is a meteorological assimilation tool that employs available observations (meteorological networks, radar, satellite, soundings, and aircraft) to generate a spatially distributed, three-dimensional representation of atmospheric features and processes. As with any diagnostic representation, it is important to ascertain how LAPS outputs deviate from a variety of independent observations. A number of surface observations exist that are not used in the LAPS system, and they were employed to assess LAPS surface state variable and precipitation analysis performance during two consecutive years (1 September 2001–31 August 2003). LAPS assimilations accurately depicted temperature and relative humidity values. The ability of LAPS to represent wind speed was satisfactory overall, but accuracy declined with increasing elevation. Last, precipitation estimates performed by LAPS were irregular and reflected inherent difficulties in measuring and estimating precipitation.

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Zoltan Toth, Mark Tew, Daniel Birkenheuer, Steve Albers, Yuanfu Xie, and Brian Motta
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Steven C. Albers, John A. McGinley, Daniel L. Birkenheuer, and John R. Smart

Abstract

The Local Analysis and Prediction System combines numerous data sources into a set of analyses and forecasts on a 10-km grid with high temporal resolution. To arrive at an analysis of cloud cover, several input analyses are combined with surface aviation observations and pilot reports of cloud layers. These input analyses am a skin temperature analysis (used to solve for cloud layer heights and coverage) derived from Geostationary Operational Environmental Satellite IR 11.24-µm data, other visible and multispectral imagery, a three-dimensional temperature analysis, and a three-dimensional radar reflectivity analysis derived from full volumetric radar data. Use of a model first guess for clouds is currently being phased in. The goal is to combine the data sources to take advantage of their strengths, thereby automating the synthesis similar to that of a human forecaster.

The design of the analysis procedures and output displays focuses on forecaster utility. A number of derived fields are calculated including cloud type, liquid water content, ice content, and icing severity, as well as precipitation type, concentration, and accumulation. Results from validating the cloud fields against independent data obtained during the Winter Icing and Storms Project are presented.

Forecasters can now make use of these analyses in a variety of situations, such as depicting sky cover and radiation characteristics over a region, three-dimensionally delineating visibility and icing conditions for aviation, depicting precipitation type, rain and snow accumulation, etc.

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Isidora Jankov, Lewis D. Grasso, Manajit Sengupta, Paul J. Neiman, Dusanka Zupanski, Milija Zupanski, Daniel Lindsey, Donald W. Hillger, Daniel L. Birkenheuer, Renate Brummer, and Huiling Yuan

Abstract

The main purpose of the present study is to assess the value of synthetic satellite imagery as a tool for model evaluation performance in addition to more traditional approaches. For this purpose, synthetic GOES-10 imagery at 10.7 μm was produced using output from the Advanced Research Weather Research and Forecasting (ARW-WRF) numerical model. Use of synthetic imagery is a unique method to indirectly evaluate the performance of various microphysical schemes available within the ARW-WRF. In the present study, a simulation of an atmospheric river event that occurred on 30 December 2005 was used. The simulations were performed using the ARW-WRF numerical model with five different microphysical schemes [Lin, WRF single-moment 6 class (WSM6), Thompson, Schultz, and double-moment Morrison]. Synthetic imagery was created and scenes from the simulations were statistically compared with observations from the 10.7-μm band of the GOES-10 imager using a histogram-based technique. The results suggest that synthetic satellite imagery is useful in model performance evaluations as a complementary metric to those used traditionally. For example, accumulated precipitation analyses and other commonly used fields in model evaluations suggested a good agreement among solutions from various microphysical schemes, while the synthetic imagery analysis pointed toward notable differences in simulations of clouds among the microphysical schemes.

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Hongli Jiang, Steve Albers, Yuanfu Xie, Zoltan Toth, Isidora Jankov, Michael Scotten, Joseph Picca, Greg Stumpf, Darrel Kingfield, Daniel Birkenheuer, and Brian Motta

Abstract

The accurate and timely depiction of the state of the atmosphere on multiple scales is critical to enhance forecaster situational awareness and to initialize very short-range numerical forecasts in support of nowcasting activities. The Local Analysis and Prediction System (LAPS) of the Earth System Research Laboratory (ESRL)/Global Systems Division (GSD) is a numerical data assimilation and forecast system designed to serve such very finescale applications. LAPS is used operationally by more than 20 national and international agencies, including the NWS, where it has been operational in the Advanced Weather Interactive Processing System (AWIPS) since 1995.

Using computationally efficient and scientifically advanced methods such as a multigrid technique that adds observational information on progressively finer scales in successive iterations, GSD recently introduced a new, variational version of LAPS (vLAPS). Surface and 3D analyses generated by vLAPS were tested in the Hazardous Weather Testbed (HWT) to gauge their utility in both situational awareness and nowcasting applications. On a number of occasions, forecasters found that the vLAPS analyses and ensuing very short-range forecasts provided useful guidance for the development of severe weather events, including tornadic storms, while in some other cases the guidance was less sufficient.

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