Improving Reservoir Management Using the Storm Precipitation Analysis System (SPAS) and NEXRAD Weather Radar

Transcription

1 Improving Reservoir Management Using the Storm Precipitation Analysis System (SPAS) and NEXRAD Weather Radar Bill D. Kappel, Applied Weather Associates, LLC, Monument, CO Edward M. Tomlinson, Ph.D., Applied Weather Associates, LLC, Monument, CO Douglas M. Hultstrand, Applied Weather Associates, LLC, Monument, CO Tye W. Parzybok, Metstat, Inc., Windsor, CO HydroVision International July 23-26, Denver, Colorado

2 Overview of the Storm Precipitation Analysis System Real-Time (SPASRT) Inputs to SPASRT Gauge data Radar data Basemap Parameter file Today s Presentation Dynamic radar-rainfall (ZR) relationships Examples Validation/Comparison Output by-products

3 Storm Precipitation Analysis System in Real-Time (SPASRT) A comprehensive, state-of-the-science gridded precipitation analysis system High resolution based on rain gauge data, radar data and climatological basemaps Developed in 2002 as a post-storm analysis system Real-time version was developed in 2009 Utilizes a GIS spatial analysis engine Analyzed over 200 storms and been operating in realtime in 4 locations

4 SPAS Storm Analysis Locations

5 Gauge Input SPASRT uses daily and hourly precipitation data to achieve the highest level of spatial and temporal detail possible MADIS (Meteorological Assimilation Data Ingest System) A clearinghouse of data from a variety of sources, including: Automated Local Evaluation in Real Time (ALERT) networks, Remote Automated Weather Stations (RAWS) stations, NOAA/National Weather Service networks, Automated Surface Observing Systems (ASOS), municipal networks, flood control districts, utility companies, CoCoRaHS, etc.

6 Gauge QC Gauge data is subjected to 4 tiers of quality control (QC) MADIS Level 1 QC Highest level of QC for precipitation data Considered a gross error check The level 1 validity checks restrict each observation to falling within a specified set of tolerance limits Spatial QC SPASRT utilizes an innovative and effective technique for identifying gauge data that are inconsistent with surrounding stations Precipitation amounts that are vastly different than the overall magnitude of the percent of a basemap are identified and removed The threshold for omitting stations is variable depending on season and location. Statistical QC Utilizing a default ZR relationship, stations are identified that are greater than X number of standard deviations from the mean difference High radar reflectivity, but no precipitation Zero precipitation gauge reports that are grossly inconsistent with the radar data are identified and removed

7 MADIS Level 1 QC not shown Spatial QC red dots Statistical QC brown dots High radar reflectivity, but no precipitation orange dots Gauge QC

8 NEXRAD Radar Reflectivity (Z) NEXRAD data Provided by Weather Decision Technologies (WDT) WDT uses advanced algorithms for mosaicing Z from multiple radar sites and overcoming common radar errors (blockage, clutter, etc.) SPASRT imposes further QC on the WDT grids i.e. infilling of beam blockages RAW Z QC ed Z

9 Basemaps Gauge+Climatologically-aided Precip. Gauge-adjusted Radar Precip. Hourly precipitation grid is created based on gauge data and a climatologicallyaided spatial interpolation technique. Basemap options include: PRISM mean ( ) monthly/annual precipitation Actual monthly Precipitation (e.g. December 2007) Precipitation frequency (e.g. 100-year 24-hour) Blended Precip. Hourly precipitation grid is created based on the observed gauge data and radar data. To account for local biases in the ZR relationship and among complex terrain, a dynamicallychanging basemap is used to create an adjustment grid. The basemap is based on a default ZR precip grid and a climatological grid. If radar data is available, SPASRT blends the climatologicallyaided and gauge-adjusted radar precip. to form a final, seamless 1-hour precip. grid, otherwise the climatologicallyaided grid servers as the final 1-hour precip. grid.

10 Basemap Integration Basemaps include: PRISM mean ( ) monthly/annual precipitation Actual monthly Precipitation (e.g. December 2007) Precipitation frequency (e.g. 100-year 24-hour) Without base map With base map (Mean Monthly Precipitation)

11 Climatologically-aided Basemap and Radar The value of radar Radar-only The value of a basemap Blended radar & climatologically-aided Radar reflectivity

12 ZR Relationship Reflectivity-rainfall (ZR) relationships are computed using a complex set of thresholds, rules and algorithms to compute rainfall rates from radar reflectivity Instead of adopting a standard (e.g. 300^1.4) ZR relationship, SPAS computes and applies a ZR relationship each hour

13 Dynamic ZR Relationship Hurricane Gustav September 1 5, 2008 Southern Texas

14 ZR Relationship (cont.) ZRs for a 99 hour Pineapple Express storm in Southern California vs. default ZR ( Orographic rain -West Z=75R 2.0 )

15 SPASRT Radar Blockage Basemap Concept Western Washington, USA Jan. 15, 2010 Storm Total Precipitation National Weather Service Northwestern Washington Storm Total Precipitation SPAS Northwestern Washington

16 SPASRT vs. Radar-only Precipitation Radar-only 24-hr Precipitation SPASRT 24-hr Precipitation

17 2-yr 24-hr 5-yr 24-hr 10-yr 24-hr SPASRT Output By-Product Average Recurrence Interval (ARI) To make precipitation data more meaningful, SPASRT includes an innovative technique for translating near real-time precipitation into an average recurrence interval (ARI) ARI = the average period between events of a particular magnitude and duration Probability in any given year = 1/ARI (e.g. 1/50-year = 0.2% chance) Technical Paper 40 Precipitation Frequency Estimates 24-hour QPE

18 SPASRT Output By-Products Average Recurrence Interval (ARI) Severe Flooding Southeast U.S. rainfall and average recurrence interval for 24-hour period ending at 21-Sep :00 AM EDT

19 SPASRT Output By-Product Depth-Area-Duration (DAD) SPASRT includes an innovative technique for translating near real-time precipitation into a deptharea-duration plot.

20 Quantitative Precipitation Forecasts (QPFs) 1-hour QPFs out 120-hours ~ 1.5 km x ~1.5 km resolution

21 SPASRT Output Basin Averaged or Gridded Precipitation QPE QPF QCed gauge data

22 Summary Careful use of radar data increases the spatial and temporal detail of rainfall information vs. the use of rain gauges only. The blend of radar- and basemap-based approaches allows for consistent precipitation patterns across complex terrain. SPASRT demonstrates the advantages deriving ZR relationships each hour rather than adopting a default ZR. Radar data can be used to QC gauges. Easy integration into hydrologic models. Convey precipitation data in terms of an ARI and DA plots. Bill Kappel billkappel@appliedweatherassociates.com (719)