[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-565489-105":53,"doc-detail-565489-en":126},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":119,"head_meta":121,"extra_data":123,"updated_unix":125},105,"en","ic62-part-2-strength-and-weaknesses-of-p-type-algorithms","IC6.2 Part 2 - Strength and Weaknesses of P-Type Algorithms","","IC6 lesson 2, Part 2 explains the strength and weaknesses of P-type algorithms used in winter weather decision training. The outline covers the Bourgouin Method and the Partial Thickness Method, focusing first on how Bourgouin was developed from collocated precipitation and upper-air sounding data. It details assumptions about vertical temperature structure, residence time, and constant vertical motion, then describes how positive and negative areas relative to the 0°C isotherm drive precipitation type decisions and thresholds for rain versus snow.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":11,"@type":70,"position":76},"https://docshare.wps.com/template/presentations/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/ic62-part-2-strength-and-weaknesses-of-p-type-algorithms/565489/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/ic62-part-2-strength-and-weaknesses-of-p-type-algorithms/565489.png","ImageObject",442,249,{"name":88,"@type":89},"ahduag","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-19","2026-10-13",true,{"@type":98,"interactionType":99,"userInteractionCount":76},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"What data and conditions were used to develop the Bourgouin Method?","Question",{"text":108,"@type":109},"It was developed using cases where surface precipitation observations were collocated with upper air soundings, with required timing within one hour and at the same location. It also relied on a vertical temperature structure from the soundings.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"What are the key parameters and assumptions in the Bourgouin Method?",{"text":113,"@type":109},"The method is driven by the mean temperature of a layer and the residence time of the hydrometeor in that layer. It assumes constant vertical motion and terminal velocity, which can be a weakness because different crystal structures have different fall speeds.",{"name":115,"@type":106,"acceptedAnswer":116},"How does the Bourgouin Method decide between rain and snow?",{"text":117,"@type":109},"It interrogates the positive area at the surface. If the surface-based positive area is less than about 5.5 J/kg it predicts snow; greater than about 13 J/kg it predicts rain; between those values it assumes a rain/snow mix.","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},565489,1792359974,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":8,"category_name":11,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":76,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":135,"language":136,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":61,"update_tm":140,"read_time":141},3985747870414,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","AWOC Winter Weather Track FY06  \n\n| 1. IC6.2 Part 2: Strength and Weaknesses of P-Type Algorithms\u003Cbr>Instructor Notes: Welcome to IC6 lesson 2, Part 2. This lesson is about 30 slides long and will take approximately 25 minutes to complete. |  |\n| --- | --- |\n| Student Notes: | |\n|  |  |\n| 2. Outline\u003Cbr>Instructor Notes: Lesson 2 of IC 6 is divided into five sections. In Part 1 we discussed the learning and performance objectives, the utility of p-type algorithms, and then we focused on the Baldwin technique and the Ramer technique. Now, in Part 2, we will discuss the Bourgouin Method and Partial Thickness Method. |  |\n| Student Notes: | |\n\n3. What about Bourgouin Method?  \nInstructor Notes: This method was developed by Bourgouin of the Canadian Meteorological Centre in Dorval, Quebec in the early 1990’s and it has been in use in the Meteo  \nIC6 .2 Part 2: Strength and Weaknesses of P-Type Algorithms 6-53  \nWarning Decision Training Branch  \nrological Services of Canada since the mid 1990’s. The method was developed by looking at a data set of cases in which surface precipitation observations were collocated with upper air soundings. The upper air soundings provided the vertical temperature structure needed to establish the criteria utilized in the method. RAOB drift was not accounted for and values were assumed to be above the sounding site. The precipitation had to be reported within one hour of the time that the sounding was taken and at the same location for it to be included in the data set utilized to tune the method. This criteria and the fact that the technique was tuned over only two winter seasons (1989-90 and 1990-91) resulted in a small training data set. For example, it was initially tuned to 54 cases of freezing rain versus ice pellets, 119 cases of rain versus snow, and 3-5 cases of ice pellets versus rain. The number of cases with respect to ice pellets versus rain is uncertain in that there is no information on the exact state of the hydrometeor aloft, (i.e. , as it enters a surface based warm layer) so the number of cases is estimated.  \nStudent Notes:  \n4. Bourgouin Method – the Basics  \nInstructor Notes: The method is built on the premise that the temperature variation of a falling hydrometeor and its resulting phase changes are predominately driven by the temperature of the environment through which the hydrometeor falls. Clearly, a hydrometeor falling through a layer with temperatures above freezing temperatures will become liquid if it resides in this layer for a sufficient time. Given this premise, the method assumes that two parameters drive p-type, the mean temperature of a layer and the residence time of the hydrometeor in this layer. The mean temperature of a given layer can be readily computed for both observed and forecast soundings. The same cannot be said of the residence time of the hydrometeor in a layer as the residence time is dependant on the depth of the layer, the terminal velocity of the hydrometeors, and the mean vertical motion. Therefore, the method assumes a constant vertical motion and terminal velocity. The assumption of a constant terminal velocity could be considered a weakness as different crystal structures have different falls speeds. However, it is not clear how significant this variation could be or how one could account for it operationally.  \n6-54 IC6 .2 Part 2: Strength and Weaknesses of P-Type Algorithms  \nAWOC Winter Weather Track FY06  \nStudent Notes:  \n5. Bourgouin Method – the Basics  \nInstructor Notes: Given these concepts it is clear that one can combine the mean temperature of a layer and the height of a layer by multiplying them together which will give you a value proportional to the area covered by the layer in question. Essentially, the algorithm determines if there is sufficient energy to melt or freeze hydrometeors as they fall. A positive area is defined as the area between the 0 degree C isotherm and the environmental temperature in the above freezing layer","cbCairhgTGuFrbb8","https://ap.wps.com/l/cbCairhgTGuFrbb8","pdf",7329194,20,"English","# Outline\n## What about Bourgouin Method?\n## Bourgouin Method – the Basics\n## Bourgouin Method – Rain vs. Snow","[{\"question\":\"What data and conditions were used to develop the Bourgouin Method?\",\"answer\":\"It was developed using cases where surface precipitation observations were collocated with upper air soundings, with required timing within one hour and at the same location. It also relied on a vertical temperature structure from the soundings.\"},{\"question\":\"What are the key parameters and assumptions in the Bourgouin Method?\",\"answer\":\"The method is driven by the mean temperature of a layer and the residence time of the hydrometeor in that layer. It assumes constant vertical motion and terminal velocity, which can be a weakness because different crystal structures have different fall speeds.\"},{\"question\":\"How does the Bourgouin Method decide between rain and snow?\",\"answer\":\"It interrogates the positive area at the surface. If the surface-based positive area is less than about 5.5 J/kg it predicts snow; greater than about 13 J/kg it predicts rain; between those values it assumes a rain/snow mix.\"}]","IC6.2 Part 2 - Strength and Weaknesses of P-Type Algorithms | PDF",1791854971,7]