University at Albany Dept. of Atmospheric & Environmental Sciences Using GAZPACHO to create forecast snowfall bias/error maps stratified by flow regime Joe Villani NWS Albany, NY Mike Main University at Albany Dept. of Atmospheric & Environmental Sciences NROW – November 1-2, 2017

What is GAZPACHO? Not talking about cold soup here… GAZPACHO = Gridded Automated Zonal Precipitation And Complete Hi-res Output GAZPACHO is an automated program that was created to assist WFOs with snowfall verification (rainfall added)

What is GAZPACHO? Creates maps of: Observed precipitation (rain, snow or both) Zone-average rain/snow NDFD forecast rain/snow (from NOMADS) Difference (or error) maps of forecast minus observed rain/snow (inches and %) Spreadsheet table of zone-average statistics

What is GAZPACHO? Created by Danny Gant (GSP), Vasil Koleci (ALY) & Joe Villani (ALY) Uses ArcGIS software and Python scripts GAZPACHO is run on a PC (with ArcGIS 10.5 installed) via a simple GUI or command line

What is GAZPACHO? Three main input options (along with start/end time of precipitation event: A QC’d list of snow (or rain) reports from home WFO (preferably with a few from surrounding WFOs too) via a PNS with metadata (LAT/LON) NOHRSC snowfall or AHPS rainfall analyses as the input source blend of NOHRSC/AHPS and PNS data

Method for Bias/MAE maps Run GAZPACHO using archived snowfall reports at ALY (55 total events from 2013-17) Use difference maps for each event in database: Sum difference maps (using raster calc in ArcGIS) to compute bias Sum of absolute value of error maps divided by # of events to compute mean absolute error (MAE)

Method for flow regime stratification Stratify by wind direction & speed at 850/925 mb: 4 quadrants for wind direction 3 categories for speed = 12 combined categories 0° 270° 90° 180° Speed category 1 0-19 kt 2 20-39 kt 3 40+ kt

Method for flow regime stratification Use Albany, NY sounding data to determine wind at 925 & 850 mb Sounding time (0000 UTC or 1200 UTC) closest to midpoint of each event

Method for flow regime stratification Stratify events into the 12 categories using a spreadsheet

Bias/MAE maps by category Compute bias/MAE (using ArcGIS) for each category using 925 & 850 mb wind Speed Category 1 0-19 kt 2 20-39 kt 3 40+ kt

Terrain influence on precip Terrain influence (upslope/down slope) on precip likely influenced more by 925 mb winds than 850 mb due to ALY’s mountains all below 850 mb Focus on 925 mb flow regimes here

Notable 925 mb categories Category ii-2 (90-180°, 20-39 kt) 5 events Negative bias NW half of area Positive bias SE of Albany Possible terrain influence: under- forecast upslope southern Adirondacks, Saratoga, southern Greens

Notable 925 mb categories Category iii-2 (180-270°, 20-39 kt) 8 events Positive bias leeward side of southern Adirondacks Negative bias SW upslope areas Fairly good upslope & downslope signatures noted

Notable 925 mb categories Category iv-2 (270-360°, 20-39 kt) 11 events Lake Effect event position error Significant negative bias terrain influence for W/NW flow events: Catskills, Taconics, Berkshires, Litchfield Hills Lake Effect contribution Includes 2016 Nov. 20-22 extreme upslope event

Notable 925 mb categories Category iv-2 (270-360°, 20-39 kt) 11 events MAE good(+/- 0 to 1”) in Hudson Valley Greater error in higher terrain

2016 Nov. 20-22 Upslope Event Extreme snowfall differences in short distances Weighted NW flow category bias/MAE

Use as a Forecast Application Use bias/MAE maps in operations Envision using maps as a forecast tool Look at forecast 850/925 mb wind direction/speed at ALB around the midpoint of upcoming event Reference bias/MAE maps based on category Bias Speed category 925 mb 1 0-19 kt 2 20-39 kt 3 40+ kt MAE

Future Work Expand database with each additional winter season Ideas for improving limitation of using single point (ALY sounding) for wind stratification Stratify events based storm track?

Questions/Comments? Joe.Villani@noaa.gov