The Storm Precipitation Analysis System (SPAS)

Locations of SPAS-Analyzed Storm Events

The Storm Precipitation Analysis System (SPAS) is a state-of-the-science 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 (see map below) for purposes of runoff model calibration/validation, storm reconstructions for legal cases, and computing depth-area-duration (DAD) tables/curves for Probable Maximum Precipitation (PMP) projects. SPAS has unique capabilities, increased accuracy over all other radar-based precipitation products, and unmatched reliability.  SPAS is the industry’s best gauge-adjusted and radar-calibrated rainfall system. Utilizing sophisticated quality control algorithms, SPAS utilizes real-time rain gauge observations, the industry’s highest resolution NEXRAD radar data from Weather Decision Technologies (WDT) and a climatological “basemap” approach to produce gridded rainfall across any terrain every 5-minutes.

SPAS Flowchart.
SPAS-NEXRAD flowchart.

Setup

Prior to a SPAS analysis, careful definition of the storm analysis domain and time frame to be analyzed is established.  Several considerations are made to ensure the domain (longitude-latitude box) and time frame are sufficient for the given application.  For PMP applications, it is important to establish an analysis domain that completely encompasses a storm center, meanwhile hydrologic modeling applications are more concerned about a specific basin, watershed or catchment.

Ideally, the analysis time frame, also referred to as the Storm Precipitation Period (SPP), will extend from a dry period through the target wet period then back into another dry period.  This is to ensure that total storm precipitation amounts can be confidently associated with the storm in question and not contaminated by adjacent wet periods.  If this is not possible, a reasonable time period is selected that is bounded by relatively lighter precipitation.  The time frame of the hourly data must be sufficient to capture the full range of daily gauge observational periods in order for the daily observations to be disaggregated into estimated incremental hourly values.

Data

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.  Based on the SPP and analysis domain, hourly and daily precipitation gauge data are extracted from our in-house database as well as the Meteorological Assimilation Data Ingest System (MADIS).  Our in-house database contains data dating back to the late 1800s.

Hourly Precipitation Data

Our hourly precipitation database is largely comprised of data from NCDC, but also precipitation data from other mesonets and meteorological networks (e.g. ALERT, Flood Control Districts, etc.) that we have collected and archived as part of previous studies.  Meanwhile, MADIS provides data from a large number of networks across the U.S., including NOAA’s HADS (Hydrometeorological Automated Data System), numerous mesonets, the Citizen Weather Observers Program (CWOP), departments of transportation, etc. (see http://madis.noaa.gov/mesonet_providers.html for a list of providers).  Although our automatic data extraction is fast, cost-effective and efficient, there are often other available precipitation data for a storm event.  For this reason, a thorough “data mining” effort is undertaken to acquire all available data from sources such as U.S. Geological Survey (USGS), Remote Automated Weather Stations (RAWS), Community Collaborative Rain, Hail & Snow Network (CoCoRaHS), National Atmospheric Deposition Program (NADP), Clean Air Status and Trends Network (CASTNET), local observer networks, Climate Reference Network (CRN), Global Summary of the Day (GSD) and Soil Climate Analysis Network (SCAN). Furthermore, we have access to an exclusive dataset of hourly precipitation data through a strategic alliance between MetStat® and Synoptic Corp.

Daily Precipitation Data

Our daily database is largely based on NCDC’s TD-3206 (pre-1948) and TD-3200 (1948 through present) as well as SNOTEL data from NRCS.  Since the late 1990s, the Community Collaborative Rain, Hail & Snow Network (CoCoRaHS) network of more than 12,000 observes in the U.S. has become a very important daily precipitation source.  Other daily data are gathered from similar, but smaller gauge networks, for instance the High Spatial Density Precipitation Network in Minnesota.

Supplemental Precipitation Data

Gauges with unknown or irregular observation times can still be used as a “supplemental” gauge. A supplemental gauge can either be added to the storm database with a storm total and the associated SPP as the temporal bounds or as a gauge with the known, but irregular observation times and associated precipitation amounts.  For instance, if all that is known is 3” fell between 0800-0900, then that information can be entered.  Gauges or reports with nothing more than a storm total are often abundant, but in order to use them, it is important the precipitation is only from the storm period in question.  Therefore, it is ideal to have the analysis time frame bounded by dry periods.

Perhaps the most important source of data, if available, is from “bucket surveys,” which provide comprehensive lists of precipitation measurements collected during a post-storm field exercise.  Although some bucket survey amounts are not from conventional precipitation gauges, they provide important information, especially in areas lacking data.  Particularly for PMP-storm analysis applications, it is customary to accept extreme, but valid non-measured precipitation values in order to capture the highest precipitation values.

Basemap

“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.  Note that a basemap is not required as the hourly precipitation patterns can be based on a station characteristics and an inverse distance weighting technique (discussed later).  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.

Level-II radar mosaic of CONUS radar with no quality control.

WDT Level-II radar mosaic of CONUS radar with quality control

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).  Weather radar data has been in use by meteorologists since the 1960’s to estimate precipitation depths, but it was not until the early 1990’s that new, more accurate NEXRAD Doppler radar (WSR88D) was placed into service across the United States. Currently efforts are underway to convert the WSR88D radars to dual polarization (DualPol) radar.  Today, NEXRAD radar coverage of the contiguous United States is comprised of 159 operational sites and 30 in Canada.  Each U.S. radar covers an approximate 285 mile (460 km) radial extent and while Canadian radars have approximately 256 km (138 nautical miles) radial extent over which the radar can detect precipitation. The primary 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.  SPAS utilizes Level II NEXRAD radar reflectivity data in units of dBZ, available every 5-minutes in the U.S. and 10-minutes in Canada.

The WDT and National Severe Storms Lab (NSSL) Radar Data Quality Control Algorithm (RDQC) removes non-precipitation artifacts from base Level–II radar data and remaps the data from polar coordinates to a Cartesian (latitude/longitude) grid.  Non-precipitation artifacts include ground clutter, bright banding, sea clutter, anomalous propagation, sun strobes, clear air returns, chaff, biological targets, electronic interference and hardware test patterns. The RDQC algorithm uses sophisticated data processing and a Quality Control Neural Network (QCNN) to delineate the precipitation echoes caused by radar artifacts. Beam blockages due to terrain are mitigated by using 30m DEM data to compute and then discard data from a radar beam that clears the ground by less than 50m and incurs more than 50% power blockage.  A clear-air echo removal scheme is applied to radars in clear-air mode when there is no precipitation reported from observation gauges within the vicinity of the radar.  In areas of radar coverage overlap, a distance weighting scheme is applied to assign reflectivity to each grid cell, for multiple vertical levels.  This scheme is applied to data from the nearest radar that is unblocked by terrain.

Once the data from individual radars have passed through the RDQC, they are merged to create a seamless mosaic for the United States and southern Canada as shown in Figure 5.  A multi-sensor quality control can be applied by post-processing the mosaic to remove any remaining “false echoes”. This technique uses observations of infra-red cloud top temperatures by GOES satellite and surface temperature to create a precipitation/no-precipitation mask.  Figure 4 shows the impact of WDT’s quality control measures.  Upon completing all QC, WDT converts the radar data from its native polar coordinate projection (1 degree x 1.0 km) into a longitude-latitude Cartesian grid (based on the WGS84 datum), at a spatial resolution of 1 km2(~1/3rd mi2) for processing in SPAS.

SPAS conducts further QC on the radar mosaic by infilling areas contaminated by beam blockages.  Beam blocked areas are objectively determined by evaluating a total storm reflectivity grid which naturally amplifies areas of the SPAS analysis domain suffering from beam blockage.

Methodology

To obtain one hour temporal resolutions and utilize all gauge data, it is necessary to disaggregate the daily and supplemental precipitation observations into estimated hourly amounts.  SPAS uses a spatial approach for disaggregating (i.e. distribute) daily/supplemental gauge data into estimate hourly values based on true hourly gauge data.

Quality Control

Exhaustive quality control measures are taken throughout the SPAS analysis.  Below are a few of the most significant QC measures taken.

  • Mass curve check –  A mass curve-based QC-methodology is used to ensure the timing of precipitation at all gauges is consistent with nearby gauges.
  • Gauge mis-location check – Although the gauge elevation is not explicitly used in SPAS, it is however used as a means of QCing gauge location.  Gauge elevations are compared to a high-resolution 15-second DEM to identify gauges with large differences, which may indicate erroneous longitude and/or latitude values.
  • Co-located gauge QC – Care is also taken to establish the most accurate precipitation depths at all co-located daily/hourly gauges.
  • SPAS vs gauge precipitation – Comparing SPAS-calculated precipitation to observed point precipitation depths at the gauge locations provides an objective measure of the consistency, accuracy and bias.  Generally speaking SPAS is usually within 5% of the observed precipitation.  Less-than-perfect correlations between SPAS precipitation depths and observed precipitation at gauged locations could be the result of any number of issues.

Spatial Interpolation

The observed hourly and disaggregated daily/supplemental hourly precipitation data are spatially interpolated into hourly precipitation grids.  SPAS has three options for conducting the hourly precipitation interpolation, depending on the terrain and availability of radar data, thereby allowing SPAS to be optimized for any particular storm type or location.

SPAS Total Storm Precipitation: No basemap or NEXRAD-aided interpolation

SPAS Total Storm Precipitation: Basemap-aided interpolation

SPAS Total Storm Precipitation: NEXRAD-aided interpolation

Basic Approach

The basic approach interpolates the hourly precipitation point values to a grid using an inverse distance weighting squared GIS algorithm and does not use a basemap or radar data.  This is sometimes the best choice for convective storms over flat terrain when radar data is not available, yet high gauge density instills reliable precipitation patterns.  This approach is rarely used.

Basemap Approach

The “basemap” approach, also known as a climatologically-aided interpolation, utilizes the spatial patterns of the basemap to govern the interpolation between points of hourly precipitation values, while the actual hourly precipitation values govern the magnitude.  This approach to interpolating point data across complex terrain is widely used.  In fact, it was used extensively by the NWS during their storm analysis era from the 1940s through the 1970s.

Gauge-callibrated Z-R from SPAS.

NEXRAD Radar Approach

The coupling of SPAS with NEXRAD provides the most accurate method of spatially and temporally distributing precipitation.  To increase the accuracy of the results however, 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.  Also, spatial variability in the Z-R relationship is accounted for through local bias corrections.

Output

Armed with accurate, high-resolution precipitation grids, a variety of customized output can be created.  Among the most useful outputs are sub-hourly precipitation grids for input into hydrologic models.

SPAS output includes, but is not limited to:

Hourly or sub-hourly (as frequent as 5-minutes) and total storm isohyets and/or grids in a variety of GIS formats

SPAS Total Storm Precipitation Map.

Hourly or sub-hourly (as frequent as 5-minutes) and total storm isohyets and/or grids in a variety of GIS formats


SPAS produces Depth-Area-Duration (DAD) tables/curves.

Depth-area-duration (DAD) storm tables for user specified durations and area sizes

SPAS provides detailed rainfall data for stormwater modeling.

Basin average precipitation at any (5-60 minute) time interval in text files and/or clipped GIS grids

Time series plot of SPAS-precipitation at time steps as short as 5-minutes.

Precipitation time series and mass curves (at 5-60 minutes time intervals) at user-defined locations

SPAS precipitation can be converted into an equivalent Average Recurrence Interval ARI) in years.

Storm precipitation return periods for 1, 2, 3, 6, or 24-hour durations