My WebLink
|
Help
|
About
|
Sign Out
Home
Browse
Search
WMOD00282
CWCB
>
Weather Modification
>
DayForward
>
WMOD00282
Metadata
Thumbnails
Annotations
Entry Properties
Last modified
7/28/2009 2:32:29 PM
Creation date
1/8/2008 11:54:38 AM
Metadata
Fields
Template:
Weather Modification
Sponsor Name
USBR Technical Serivce Center, River Systems & Meteorology Group
Project Name
Snow Accumulation Algorithm for the WSR-88D Radar, Version 1
Title
Snow Accumulation Algorithm for the WSR-88D Radar, Version 1
Prepared For
USBR
Prepared By
Arlin B. Super and Edmond W. Holroyd
Date
6/1/1996
State
CO
Weather Modification - Doc Type
Scientific Study
There are no annotations on this page.
Document management portal powered by Laserfiche WebLink 9 © 1998-2015
Laserfiche.
All rights reserved.
/
115
PDF
Print
Pages to print
Enter page numbers and/or page ranges separated by commas. For example, 1,3,5-12.
After downloading, print the document using a PDF reader (e.g. Adobe Reader).
Show annotations
View images
View plain text
<br />1. Introduction <br /> <br />The OSF (Operational Support Facility) of the WSR-88D Radar Program released a request <br />for proposals in the fall of 1994 seeking development of a snow accumulation algorithm for <br />the new national network of Doppler weather radars. Reclamation (Bureau of Reclamation) <br />submitted a proposal in mid-October 1994, which was evaluated along with proposals from <br />other Federal and Federally-supported agencies. An MOU (memorandum of understanding) <br />was signed the end of May 1995 among the NEXRAD (NEXt generation RADar) Program, <br />the WSR-88D OSF, and Reclamation, which called for Reclamation to develop an algorithm <br />over a 3-year period to estimate both S (snow water equivalent) and SD (snow depth) from <br />radar measurements. (Snow depth is sometimes called snow accumulation-in this report, <br />snowfall accumulation refers to S accumulation for 1 hour or more.) This snow accumulation <br />. algorithm (hereafter Algorithm) is to be limited to dry snowfall. The complications of dealing <br />with mixed rain and snow and/or melting snow with associated "bright band" effects is <br />beyond the scope of the requested work and the resources available to accomplish it. <br /> <br />The original MOU was amended in August 1995 to include precipitation data collection <br />parallel to the south shore of Lake Erie east-northeast of the Cleveland, Ohio, WSR-88D <br />radar. Subsequent analysis of these snowfall and radar measurements was expected to <br />evaluate the ability of the developing Algorithm to detect and quantify lake effect snowfalL <br /> <br />This report discusses progress during the first year of effort ending June 1, 1996. Three <br />letter-type quarterly reports have been submitted to the OSF which provide more detail about <br />some efforts. <br /> <br />Z =aSJ} <br />.' <br /> <br />(1) <br /> <br /> <br />This report is organized around the tasks to be performed by Reclamation scientists during <br />the first year ending May 31, 1996, as spelled out in the MOU's SOW (statement of work). <br />Briefly, the MOU tasks are: <br /> <br />1. Scrutinize existing precipitation gage observations of S from the 1994-95 winter within <br />reasonable range of WSR-88D systems with Level II data. Level II data, recorded on <br />8-~ tape, are the most basic data available to researchers (Crum et al., 1993). <br /> <br />2. Obtain Level II data from selected WSR-88D systems and storm periods for the 1994-95 <br />winter that have corresponding gage data. Also obtain supporting software from the OSF <br />for manipulation of these data and hardware suitable for working with these data and <br />software. Progress under tasks No. 1 and 2 is discussed in section 2 of this report. <br /> <br />3. Use the data, software, and hardware of tasks No. 1 and 2 above and write additional <br />software as needed for development of a "simplified prototype" Algorithm for prediction <br />of S from WSR-88D Level II data. The initial Algorithm will be based on comparisons of <br />radar measurements of equivalent (also called effective) reflectivity factor, Ze' with surface <br />gage measurements of S. The Algorithm will incorporate radar-estimated horizontal wind <br />speed and direction for advection of falling snow particles to match surface observations <br />of S with radar bin observations of Ze' A large number of Ze-S pairs will be used to <br />calculate the empirically-determined coefficients, a and f3, for the commonly-used power- <br />law model: <br />
The URL can be used to link to this page
Your browser does not support the video tag.