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EXTENDED ABSTRACT FOR 5TH INTERNATIONAL SYMPOSIUM ON TROPOSPHERIC PROFILING

TITLE: The LAPS Water in All Phases Analysis: the Approach and Impacts on

Numerical Prediction

By

John McGinley, NOAA Forecast Systems Lab

Steve Albers*, Dan Birkenheuer, Brent Shaw*, Paul Schultz

NOAA Forecast Systems Lab

Boulder, CO

and

CIRA, Colorado State University*

Ft. Collins, CO

 

  1. Introduction

    2. The Local Analysis and Prediction System (LAPS: McGinley, et al., 1992) was developed as a go-anywhere assimilation system for local weather offices. LAPS is a UNIX-based set of software designed to accept all sources of local data: satellite, mesonet, profiler, radar, aircraft, etc. and provide high resolution analyses on a variety of computer hardware platforms. Current application is focused on retrieval of the cloud and moisture environment, leading to diabatic initialization of MM5. LAPS has been ported to a number of sites worldwide. One of the unique components of LAPS is the cloud analysis (Albers, et al. 1996), designed to provide the user with a complete description of the cloud environment: bases, tops, coverage, liquid, ice and precipitation distribution. This cloud scheme has been used and adapted by others (Zhang, et al., 1998). In 1999 the LAPS effort was focused on expanding the cloud scheme to provide a complete analysis of water in all phases (WIAP) with an aim toward generating model initial conditions with a fully diagnosed water environment and the appropriate motions and thermodynamics to sustain it.

     

  2. The LAPS Analysis Scheme

    3. A. State variables

    LAPS uses a two stage approach to analysis: a) a data combination step where data from many platforms is combined to satisfy basic geometric constraints through a combination of successive corrections methods and variationally applied splines ( Albers, et al., 1996; McGinley, 1982); and b) a variational dynamic adjustment step (McGinley, 1987) that forces the fundamental equations (thermodynamics , motion, and continuity ) to be satisfied within the domain to a desired level of accuracy.

    B. Cloud analysis and microphysical retrieval

    Albers, et al. (1996) describes the cloud analysis scheme. Briefly, the process utilizes multi-spectral satellite data from GOES, radar, aircraft, surface reports, and the LAPS temperature analysis, to derive a 3-dimensional estimate of cloud coverage. The routine is based on utilizing hypothesized "cloud soundings" from the data sources and horizontal interpolation using a successive corrections method. The net result is a 3-dimensional depiction of the cloud field (fig 1a). An additional step is the retrieval of cloud microphysical data using a streamlined version of the Smith-Feddes model described by Haines, et al. (1989). Clouds are typed utilizing a table based on stability and temperature, and from this information vertical motion is inferred. This vertical motion is input as an "observed" quantity in the variational analysis. The cloud field is integrated with analysis of water vapor (Birkenheuer, 1999) and dynamical balancing, producing a three dimensional depiction that represents water in all phases: water vapor fields, a consistent partitioning of ice and water distribution and precipitation, along with the appropriate horizontal and vertical wind perturbations and the associated hydrostatic temperature perturbations.

  3. Model initialization with LAPS

With WIAP offering a detailed moisture field and the balancing scheme mitigating potential model shock, the LAPS fields were used for generation of an initial condition for 4-times- daily MM5 (v3) model runs. The initial fields ("hot start") represent a state that contains water and ice components of clouds and precipitation, the vertical and horizontal motions that sustain them, and a balance condition that ensures a smooth model start. As a comparison additional runs were performed with MM5 initialized by nudging to a series of three (hourly) LAPS analyses ("warm start"); and a control run that utilized only the background Eta model run as a non-LAPS initial condition ("cold start"). As an example, Figure 1 a-b illustrates a case for 11 July 2000 where the LAPS cloud analysis is shown for the initial time and the subsequent 1-hour cloud forecast. The "hot start" has allowed mature precipitating systems to be present within the first hour of the forecast.

 

Fig 1a: MM5 initial condition for 11 July 00 06GMT derived from LAPS moisture analysis. Figure on right shows IR cloud field for same time.

 

Fig 1b: MM5 1-hour forecast for 11 July 00 07GMT. Figure on right shows IR cloud field. Note that most intense clouds are present very early in forecast. No spin up was necessary.

 

Figures 2a-b show Probability of Detection (POD ) of clouds and reflectivity greater than 35dBz for Oct 00 for forecasts over the LAPS domain shown in Fig 1. Note how the hot start (solid line) begins with accurate portrayal of cloud and precipitation systems while cold start (dotted line) must spin up for 3-5 hours. Also shown is the warm start (dashed). Work is underway to reduce the spin down of precipitating systems seen in 2b.

Fig 2a: POD for Clouds vs forecast time for Hot start (solid), nudged(dashed) and cold start (dotted)

Fig 2b: POD for Reflectivity > 35dbz vs forecast time for Hot start (solid), nudged(dashed) and cold start (dotted)

 

IV. Summary

Overall the technique shows promise and is very computer efficient. The presentation will show more extensive, updated, and longer term results, and future directions.

 

 

References

Albers, S., J. McGinley, D. Birkenheuer, and J. Smart, 1996: The Local Analysis and Prediction System (LAPS): Analysis of clouds, precipitation, and temperature, Weather and Forecasting, 11, 273-287

Birkenheuer, D. 1999: The effect of using digital satellite imagery in the LAPS Moisture analysis, Weather and Forecasting, 14, 782-788

McGinley, J., S. Albers and P. Stamus, 1992: Local Data Assimilation and Analysis for Nowcasting, Adv. Space Res., 12,No 7, 179-188

McGinley J., 1982: A diagnosis of Alpine Lee Cyclogenesis, Mon. Wea. Rev. 110, 1271-1287

McGinley J., 1987: A variational objective analysis system for analysis of the AlPEX data set., Meteor. Atmos. Phys. 36, 5-23.

Zhang, J., F. Carr, K. Brewster, 1998: The ADAS Cloud Analysis, Preprints, 12th Conference on Numerical Weather Prediction, Phoenix, AZ, Amer. Met. Soc.185-188