Improving Hydrologic Analysis and Applications through the Use of - PowerPoint PPT Presentation

Improving Hydrologic Analysis and Applications through the Use of Quality Controlled Radar Data and the Storm Precipitation Analysis System � Douglas M. Hultstrand, Metstat, Inc., Windsor, CO � Beth Clarke, Weather Decision Technologies, Inc., Norman, OK � Tye W. Parzybok, Metstat, Inc., Windsor, CO � Edward M. Tomlinson, Ph.D., Applied Weather Associates, LLC, Monument, CO � Bill D. Kappel, Applied Weather Associates, LLC, Monument, CO National Hydrologic Warning Council May 18-21, 2009 Vail, CO

Outline � Background � Storm Precipitation Analysis System (SPAS) – SPAS – SPAS-NEXRAD � SPAS Output

Why Are Spatial Precipitation Estimates Important? � Crucial for hydrologic Modeling � Traditional Techniques  Calibration not Representative  Validation  Thiessen Polygon � Rain Gauges Inadequate  Inverse Distance Square  Geostatisical Techniques  Spatial/Temporal

Total Rainfall Comparison Inverse distance weighting (no radar) Default ZR relationship & no bias adjustment 7.53” 14.59” 6.51” 7.98” NWS radar-estimated rainfall SPAS-NEXRAD 13.12” 16.63” 6.17” 10.07”

Streamflow Comparison 24000 0.00 Precipitation 22000 Observed 0.05 SPAS-NEXRAD 20000 Default ZR IDW 18000 0.10 16000 Incremental Precipitation (in) 0.15 14000 Streamflow (cfs) 12000 0.20 10000 0.25 8000 6000 0.30 4000 0.35 2000 0 0.40 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100105110 Time (30-min Intervals) � Peak Discharge (Nov. 6-8, 2006)  Observed: 16,700 cfs  SPAS-NEXRAD: 16,792 cfs (+ 92 cfs)  IDW: 17,121 cfs (+ 421 cfs)  Default ZR: 18,588 cfs (+ 1,888 cfs)

Storm Precipitation Analysis System  A comprehensive, state-of-the-science gridded precipitation analysis software program  Developed and operated by meteorologists and hydometeorologists since 2002  Historically a post-storm analysis program, but is evolving into a real-time tool  Skilled in analyzing extreme storm events  Generates a plethora of output used for hydrologic applications  Has the unique capability to compute storm centered depth-area-duration (DAD) tables

SPAS Modes � SPAS operates in two modes  SPAS (pre-NEXRAD storms)  Utilizes a “basemap” for interpolating hourly storm precipitation. Basemap options include:  Precipitation Frequency grids (e.g. 100-year 24-hour) - NOAA Atlas 14, TP-40, NOAA Atlas 2, etc.)  Elevation - Digital Elevation Model (DEM)  Mean (1971-2000) monthly precipitation - P arameter-elevation R egressions on I ndependent S lopes M odel (PRISM)  PRISM Mean (1971-2000) annual precipitation  PRISM Total monthly precipitation (e.g. July 1935)  No basemap  SPAS-NEXRAD  Requires SPAS general to be run first  Uses calibrated radar data for interpolating hourly precipitation  SPAS-NEXRAD Real-Time  In development stage

SPAS Flowchart Basemap Raw gage precip. data Reformat & QA/QC Daily gage data Supp. gage data Hourly gage data Convert to hourly Convert to hourly QA/QC QA/QC QA/QC Compute % of Basemap (“isopercental”) at gages Pooled hourly gage data Spatially interpolate gage Isopercentals to a grid Yes No Radar? Repeat each hour Isopercental * Basemap = hourly precip grid SPAS-NEXRAD QA/QC Hourly precip. Grids Repeat (if necessary) Depth-Area-Duration Analysis Prelim. total storm grid QA/QC Storm center(s) mass curve Final total storm grid Other (GIS files, etc.) DAD results (timing information)

SPAS Precipitation Input � SPAS utilizes a variety of precipitation data to achieve the highest level of spatial and temporal detail possible.  Hourly data - In-house National Climatic Data Center (NCDC) database - Automated Local Evaluation in Real Time (ALERT) networks, Remote Automated Weather Stations (RAWS) stations, NWS’s Automated Surface Observing Systems (ASOS), municipal networks, flood control districts.  Daily data - In-house National Climatic Data Center (NCDC) database - Municipal networks, etc  Supplemental data - Storm total’s from “bucket survey’s”, public reports NWS Storm Data, etc.

SPAS Flowchart Basemap Raw gage precip. data Reformat & QA/QC Daily gage data Supp. gage data Hourly gage data Convert to hourly Convert to hourly QA/QC QA/QC QA/QC Compute % of Basemap (“isopercental”) at gages Pooled hourly gage data Spatially interpolate gage Isopercentals to a grid Yes No Radar? Repeat each hour Isopercental * Basemap = hourly precip grid SPAS-NEXRAD QA/QC Hourly precip. Grids Repeat (if necessary) Depth-Area-Duration Analysis Prelim. total storm grid QA/QC Storm center(s) mass curve Final total storm grid Other (GIS files, etc.) DAD results (timing information)

SPAS Methodology Daily to Hourly Precipitation � To achieve an hourly time step at ALL stations, its necessary to convert daily & supplemental stations into estimated hourly stations. � In the past, timing of daily measured data was accomplished by associating each daily station with a single nearby hourly station. � SPAS, however, uses several hourly stations to time each of the daily stations, thereby allowing the hourly precipitation distribution to be unique at each daily station. Daily data  Estimated hourly data 8 1 .2 1 .0 6 0.8 In. 4 0.6 In.  0.4 2 0.2 0 0.0 1 2 3 1 1 3 25 37 49 61 73 85 D ay H o ur � This provides more representative spatial and temporal detail.

SPAS Flowchart Basemap Raw gage precip. data Reformat & QA/QC Daily gage data Supp. gage data Hourly gage data Convert to hourly Convert to hourly QA/QC QA/QC QA/QC Compute % of Basemap (“isopercental”) at gages Pooled hourly gage data Spatially interpolate gage Isopercentals to a grid Yes No Radar? Repeat each hour Isopercental * Basemap = hourly precip grid SPAS-NEXRAD QA/QC Hourly precip. Grids Repeat (if necessary) Depth-Area-Duration Analysis Prelim. total storm grid QA/QC Storm center(s) mass curve Final total storm grid Other (GIS files, etc.) DAD results (timing information)

Methodology Base Map Concept � The base map helps interpolate values at ungauged locations in complex terrain. Without base map With base map (Mean Monthly Precipitation)

SPAS Flowchart Basemap Raw gage precip. data Reformat & QA/QC Daily gage data Supp. gage data Hourly gage data Convert to hourly Convert to hourly QA/QC QA/QC QA/QC Compute % of Basemap (“isopercental”) at gages Pooled hourly gage data Spatially interpolate gage Isopercentals to a grid Yes No Radar? Repeat each hour Isopercental * Basemap = hourly precip grid SPAS-NEXRAD QA/QC Hourly precip. Grids Repeat (if necessary) Depth-Area-Duration Analysis Prelim. total storm grid QA/QC Storm center(s) mass curve Final total storm grid Other (GIS files, etc.) DAD results (timing information)

Methodology Hourly Precipitation Grids � The hourly precipitation grids serve as the basis for much of the output statistics Hourly precipitation ending at 1600 GMT Nov. 6, 2006

SPAS Flowchart Basemap Raw gage precip. data Reformat & QA/QC Daily gage data Supp. gage data Hourly gage data Convert to hourly Convert to hourly QA/QC QA/QC QA/QC Compute % of Basemap (“isopercental”) at gages Pooled hourly gage data Spatially interpolate gage Isopercentals to a grid Yes No Radar? Repeat each hour Isopercental * Basemap = hourly precip grid SPAS-NEXRAD QA/QC Hourly precip. Grids Repeat (if necessary) Depth-Area-Duration Analysis Prelim. total storm grid QA/QC Storm center(s) mass curve Final total storm grid Other (GIS files, etc.) DAD results (timing information)

SPAS-NEXRAD Flowchart Hourly NEXRAD Pooled hourly gage data Basemap Reflectivity (Z) (R) Relate, optimize & QC QA/QC ZR relationship Compute initial precip grid using ZR algorithm Compute gage residual (Robs – Rcalc) Compute residual as % Repeat each of basemap (“isoresidual”) hour Spatially interpolate gage isoresidual to grid Isoresidual * basemap = QA/QC Bias correction grid Bias correction grid + initial precip grid = final precip grid

SPAS-NEXRAD NEXRAD 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.) � SPAS-NEXRAD imposes further QC on the WDT grids RAW Z QC’ed Z

SPAS-NEXRAD Flowchart Hourly NEXRAD Pooled hourly gage data Basemap Reflectivity (Z) (R) Relate, optimize & QC QA/QC ZR relationship Compute initial precip grid using ZR algorithm Compute gage residual (Robs – Rcalc) Compute residual as % Repeat each of basemap (“isoresidual”) hour Spatially interpolate gage isoresidual to grid Isoresidual * basemap = QA/QC Bias correction grid Bias correction grid + initial precip grid = final precip grid