Storm Precipitation Analysis System (SPAS)
MetStat originally conceptualized, developed and implemented the SPAS software in 2002 to support the growing number of Probable Maximum Precipitation (PMP) studies, but quickly became the go-to system for generating gridded precipitation for other hydrologic model calibration/verification applications. Since then however, MetStorm is the next generation of storm precipitation analysis software. Storms already analyzed with SPAS, but needed for a new studies/projects, are easily re-analyzed with MetStorm to take advantage of MetStorm’s improvements, database archiving and integration into other programs/scripts for consistency and efficiency.
The Storm Precipitation Analysis System is a hydrometeorological tool used to characterize the temporal and spatial details of precipitation events. SPAS is grounded on a decade of scientific research and development and has demonstrated reliability in post-storm analyses. SPAS has been used to analyze over 500 extreme precipitation events. SPAS has some unique capabilities and in general better accuracy over all other traditional radar-based precipitation products during the period 1995-2014; since then MetStorm has integrated newer data sets and better algorithms to more accurately quantifying precipitation.
The foundation of a SPAS analysis is the “ground truth” precipitation measurements. In fact, the level of effort involved in “data mining” and quality control represent over half of the total level of effort needed to conduct a complete storm analysis. SPAS operates with three primary data sets: precipitation gauge data, a “basemap” and, if available, radar data.
Flowchart of SPAS inputs, functionality, and outputs.
Precipitation Gauge Data – The precipitation data, which comprises of daily, hourly and supplement (irregular observations) is largely comprised of data from NCEI and other data mined sources, such as “bucket surveys,” flood control districts, USGS, etc. An example analysis based solely on gauge data is seen in the left panel of the figure below.
Basemaps – “Basemaps” are independent grids of spatially distributed weather or climate variables that are used to govern the spatial patterns of the hourly precipitation. The basemap also governs the spatial resolution of the final SPAS grids, unless radar data is available/used to govern the spatial resolution. Basemaps in complex terrain are often based on the PRISM mean monthly precipitation or HDSC precipitation frequency grids given they resolve orographic enhancement areas and micro-climates at a spatial resolution of 30-seconds (about 800 m). Basemaps of this nature in flat terrain are not as effective given the weak precipitation gradients, therefore basemaps for SPAS analyses in flat terrain are often developed from pre-existing (hand-drawn) isohyetal patterns, composite radar imagery or a blend of both. An example of an analysis using just a basemap and gauge data may be seen in the center panel of the figure below.
Radar data – For storms occurring since approximately the mid-1990’s, weather radar data is available to supplement the SPAS analysis. A fundamental requirement for high quality radar-estimated precipitation is a high quality radar mosaic, which is a seamless collection of concurrent weather radar data from individual radar sites, however in some cases a single radar is sufficient (i.e. for a small area size storm event such as a thunderstorm). Our vendor of NEXRAD weather radar data for SPAS is Weather Decision Technologies, Inc. (WDT), who accesses, mosaics, archives and quality-controls NEXRAD radar data from NOAA and Environment Canada. An example analysis that incorporates radar data as well as gauge data and a basemap is seen in the bottom-right figure.
The major distinguishing feature of SPAS to other precipitation analysis software systems is the approach used to compute precipitation from radar data. In SPAS, the concurrent quality-controlled precipitation observations are used for calibrating the radar reflectivity to rain rate relationship (Z-R relationship) each hour instead of assuming a default Z-R relationship. Although this works well when a large sample size of radar-gauge pair exist, the approach often results to the default Z-R relationship. New dual pol radar-to-precipitation algorithms used in MetStorm have been shown to better quantify precipitation depths.
The high-resolution precipitation grids produced by SPAS, provide a means of creating a variety of customized output similar to that of MetStorm.
SPAS is no longer being regularly used by MetStat; we instead use MetStorm.