Meteorological Forecast Inputs for the Western Weather Work Group

Alan FoxFox Weather, LLC

Fortuna, California, USAAugust 8, 2014

Items to be Mentioned• MtnRT System Overview• Implementing MtnRT: PNW, California, Arizona, Mexico, SW Plains• Forecast support for IPPC• Other Fox Weather Products (Rain Fcsts)• Fox MtnRT Forecast Verifications (Rain, Tmin for Frost.• Some NDFD Forecast Verifications and Considerations• IPPC Forecasts with MtnRT and NDFD grids (MyPestPage)• PRISM for MtnRT forecasts• Virtual Estimation (IPPC’s method) versus measured in and near

canopy (Temperature).• Latest PRISM Datasets

MtnRT® Overview• MtnRT accepts Fox Weather’s WRF data for inputs, then

recalculates to a local scale to produce customized products.• MtnRT is not a simple downscaling utility. • MtnRT simulates decisions of a human forecaster and

recalculates a finer scale forecast and produces innovative products.

• WRF and MtnRT work together:– WRF provides the setup (or ‘first guess’)– MtnRT provides targeted “punch” (or fine-tuned forecast)

• Products include:– Text Report: 1 hour forecast-interval through 168 hours

• Rain, Snow, RH, Wind Dir/Speed, LW, Dewpt,Temperature• Other custom parameters

– 7 Day Maps Matrix (4 for each day, day0-day6, inclusive).

MtnRT is implemented for many different regions in the western U.S. SW Great Plains, and Mexico

Temperature (degrF Rain (inches)

Gridded (1 km) Leafwetness Forecast: 1 hour increment for five 24 hour periods for input to OSU/IPPC

This work was supported by the Agriculture and Food Research Initiative Competitive Grants Program No. 2010-85605-2054 from the National Institute of Food and Agriculture.This was one of the four gridded forecast regions funded by AFRI and CDFA Grants in 2010

Region: Sonoma County to Sacramento, CA

Text Forecast Input for IPPC Models

feet

Some Other Current Work• Primary MtnRT-related work at this time is in forecasting for Quantitative

Precipitation (QPF) and other agricultural work outside of our work with IPPC.

• For our QPF services, we developed an interactive display:– (1) Web site showing our current QPF images for selected gridded forecast domains,– (2) Formatted images for display on a GIS display system.– (2) A web services interface wherein our QPFs can be displayed on a Google-Maps

interface.• We are developing our web services interface so that model output images in

our system (ref: (1) above) can be specified and run from remote web pages, such as a county flood warning system or a Cooperative Extension web site.

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Forecast Verifications• The next several slides deal with forecast verification of Rain and

Temperature.

Santa Cruz County raingauge 1hr rains from two stations:.45 to .56 at 1500, 1600, 1800 PST are close to the .55 inch indicated at Scott Creek, and Ben Lomond RAWS for 1900 and 2000 PST (3-4 hour time difference).MtnRT forecast was initialized at 1600 PST 25 Feb (00z 26 Feb).

Rain Forecast Example Feb 26, 2014 Santa Cruz Co

Scott CreekFcstd .53Obsv .46

Ben LomondFcstd .55Obsv .56

Reference:Santa Cruz County Department of Public Works 701 Ocean Street, Room 410 Santa Cruz, CA 95060Website: http://www.dpw.co.santa-cruz.ca.us/

Raingauge 1 hourrainfall from three mountain stations in the western Santa CruzMountains.

February 26, 2014 – Upslope enhancement in sub-tropical rains.Heavy rains in foothills but light rains in the valleys.

Ben Lomond

Scott Creek

Ref: Fox, A., 2011, 2014: MtnRT® White Paper.

Forecasted versus Observed Rainfall – San Gabriel Mountains Sub-tropical rain event – Dec 19-23, 2010 Forecasted 90 hours out

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Point Forecast Verifications - Potter ValleyMarch - June, 2011

Observed NDFD Forecast MtnRT Forecast

POTTER VALLEY (PTV)4/17 - 6/5 2011, inclusive...CORR (r) = 0.8024, StdDev = 2.7739, DF = 47, Npoints=49 for MtnRTCORR(r) = 0.6352, StdDev = 3.5883, DF = 46, Npoints=48 for NDFD

POTTER VALLEY (PTV)MARCH 8 - JUNE 5, 2011CORR(r) MtnRT = 0.7614, Sdev = 3.39394, Npoints= 89, DF= 87CORR(r) NDFD =0.6992, StdDev = 3.84, Npoints=79, DF = 77

Comparison of Minimum Temperatures between MtnRT and NWS NDFD Forecast: December 2009

Stockton,CAMtnRT vs.Obs NWS vs. ObsStdDev: 5.14345079 StdDev: 6.999148Corrl: 0.7526042 Corrl: 0.443968

Salinas, CAMtnRTvsObs NWS vs.ObsStdDev: 3.68449401 StdDev: 5.155623Corrl: 0.81127736 Corrl: 0.575072

Visalia, CAMtnRTvsObs NWS vs.ObsStdDev: 3.31550345 StdDev: 4.475758Corrl: 0.89437241 Corrl: 0.797088

Ramona, CAMtnRTvsObs NWS vs.ObsStdDev: 6.98554401 StdDev: 7.296632Corrl: 0.4149666 Corrl: 0.311169

Riverside, CA (March AFB)MtnRTvsObs NWS vs.ObsStdDev: 7.03402958 StdDev: 6.757285Corrl: 0.36953387 Corrl: 0.450734Ref: Fox, A., 2014: MtnRT® White Paper.

FORECAST VERIFICATIONSTMIN FOR FROST FORECASTSMtnRT® and NDFD

Potter Valley D9878 2012 Verification Data

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Potter Valley April/May 2012

OBS Tmin MtnRT Tmin NDFD Tmin

Potter Valley D9878, Apr 5 - May 8, 2012N=33, df=31 Obs AMean 42.09, Median 42, MtnRT AMean 42.42 Median 43, NDFD AMean 43.9, Median 44MtnRT obs v fcst Pearson R 0.924 StDev 2.73 Corrl .9264NDFD obs v fcst Pearson R 0.8652 StDev 3.05, corrl .8714

Ref: Fox, A., 2014: MtnRT® White Paper.

Forecast Verifications for Tmin during FrostSeasons 2012 and 2013 in the northern Russian River region. Comparison with theNDFD Tmins is also shown, with statistical analysis of performance.

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Potter Valley April/May 2013

OBS Tmin MtnRT Tmin NDFD Tmin

Potter Valley D9878 Apr 1 – May 31, 2013Pearson R obsvd v MtnRT: 0 .866

obsvd v NDFD 0.5837df=58, N=61 Obsvd: AMean 41.75 Median 40, MtnRT AMean 43.25, median 44, NDFD AMean 44.45, Median 45:polynomial regression obsv v MtnRT stdev 2.67, corrl=.88317, obs v NDFD stdev= 3.13, corrl=.7654,

Potter Valley D9878 2013 Verification Data

1. NWS spatial display of weather apparently at 2.5 km resolution2. Experimental version of NDFD display3. These biases were not related to rain.4. Are the inconsistencies due to biases introduced by differences in the forecast between WFOs? They appear to follow geopolitical boundaries.

Spatial NDFD Forecast – Temperature valid at 1100 PST 8/4/2014

Spatial NDFD Forecast – 6 hour rain ending 11am PST 8/4/2014

Predicted pattern of rain does not explain the temperature patterns In previous slide.

IPPC uses MtnRT in localized areas where terrain has a significant effect on wind speed, e.g. Hood River, OR, where the NWS NDFD forecast is less accurate.

IPPC uses MtnRT as the forecast engine, where they have implemented it in the western U.S., and NWS NDFD in other areas of the U.S. where MtnRT is not currently available.

The next slide shows an example of the IPPC website (My Pest Page) at

Credits: Leonard Coop, IPPC/Oregon State Univ.http://uspest.org/risk/models

This project was supported by the Agriculture and Food Research Initiative Competitive Grants Program No. 2010-85605-2054 from the National Institute of Food and Agriculture

IPPC’s Use of MtnRT and NDFD: “My Pest Page”

Boonville shares both coastal and inland climate regimes

Virtual Weather Station temperature version 2.0 (V2) estimates compared to measured at two elevations in

Hyslop,OR hop yard, June 17-23, 2010 and Mar 1-7, 2011

Ref: David Gent, ARS/USDA

Comparing Estimated “V2” data with actual at different elevations in plant canopy

• The previous slide shows an example of in-canopy temperature data collected in a hop yard at two heights, 1.5 m, and near the top of the trellis at 4.5 m during the 2010 and 2011 seasons.

• Temperature estimates by version 2.0 (V2) estimation procedure were generally within 1 degree C.

• Performance was most notably affected in low temperature (Tmin) estimation. The V2 estimation procedure tends to overestimate the daily Tmin, which can influence RH, dew point, and LW calculation.

• The importance of these differences for disease forecasting vary depending on the sensitivity of the disease model and time of year.

• Effects of the V2 weather estimates, or forecasted weather, on performance of disease predictions, our finding is that the importance of errors depends heavily on the specific model. Simple models that rely on a few threshold rules tend to be more sensitive to weather input errors than more complex models where the weather inputs are treated as a continuous variable rather than binary (yes/no) input. Models having wetness variables as inputs also tend to be more seriously affected than those that rely solely or primarily on temperature, the powdery mildew indices being examples of the latter.

Ref: David Gent, ARS/USDA (updated 2014)

Differences between a MtnRT forecastof Tmin (degF) (a) with PRISM delta correction,(b) without.

(a) MtnRT Point temperatures: 1) 29, 2) 39, 3) 444) Boonville RAWS obsv 35.0, adj fcst 36.0

(b) MtnRT Point temperatures: 1) 31, 2) 43, 3) 43, 4) obsvd 35.0, unadj fcst 41

MtnRT® Forecast of Minimum Temperature For Frost

17 Feb 2014

Including PRISM with MtnRT® provides:1. Better resolution down to crop scale2. Using MtnRT and PRISM togetherprovides better specificity to the forecast of Tmin.

Ref: Fox Weather, LLC

Core PRISM Datasets

800-m and 4-km grids, CONUS

• Monthly and annual normals - TMAX, TMIN, PPT

1981-2010 averages

• Monthly time series - TMAX, TMIN, PPT, TDEW

January 1895 – present, updated monthly

• Daily time series - TMAX, TMIN, PPT

1 January 1981 – present, updated daily

Ref: Christopher Daly, 2014

Reference List: MtnRT work with IPPC/OSU + WWWG PublicationsJohnson, D.A., T.F. Cummings, A.D. Fox, J.R. Alldredge, 2014: Accuracy of rain forecasts forinclusion into late blight management models. Potato Progress XIV:1, 6pp.

Fox, A.D., 2014: MtnRT White Paper: Summary of the MtnRT System. Providing weather inputsfor plant disease models. Published at www.foxweather2.com. May 2, 2014, 54 pp.

Gent, D. H., Mahaffee, W. F., Pfender, W. F., McRoberts, N. 2013. The use and role of predictive systems in disease management. Annual Review of Phytopathology. 51: 267-289

Hultstrand, D.M., T.W. Parzybok, A.D. Fox, E.M. Tomlinson, and B.D. Kappel, 2011: Use ofhigh resolution gauge-adjusted precipitation in the verification of numerical quantitativeprecipitation forecasts for west coast storm events. Presented at the National Hydrologic Warning Council Conference, May 10-12, 2011, San Diego, CA, 39 pp.

Pfender, W. F., D. H. Gent, and W. F. Mahaffee. 2011. Sensitivity of disease management decision aids to temperature input errors associated with out-of-canopy and reduced time-resolution measurements. Plant Disease 95:.726-736.

Fox, A.D., 2011: MtnRT White Paper: Summary of the MtnRT System. Providing weather inputsfor plant disease models. Published at www.foxweather2.com. August 31, 2011, 45 pp.

Pfender, W. F., Gent, D. H., Mahaffee, W. F., Coop, L. B. and Fox, A. D. 2010. Decision aids for multiple decision disease management as affected by weather input errors. Phytopathology 101:644-653.

Reference List: Cont’d

Daly, C., Coop, L., Fox, A. 2008. Novel approaches to spatial and temporal estimation of diversewestern meteorology. Symposium I: A Consortium Approach to Advancing the use of WeatherInformation in IPM: The Western IPM Weather Workgroup. American Phytopathological SocietyPacific Division Annual Meeting. June 24-27, 2008, Jackson, Wyoming.

Coop, L. and A. Fox. 2008. Novel Delivery IPM Tools in Real Time for Decision Support – Pull.Symposium I: A Consortium Approach to Advancing the use of Weather Information in IPM: TheWestern IPM Weather Workgroup. American Phytopathological Society Pacific Division AnnualMeeting. June 24-27, 2008, Jackson, Wyoming.

Fox, A.D., 2005: Using MtnRain™ and MtnRTemps™ to forecast rainfall over complex terrain inSouthern California winter storms during Winter 2004-05. Electronic Newsletter Supplement,Floodplain Management Association, October 2005, 25 pp.

Final Summary• A brief description of weather inputs into the IPPC system has been given.• A sample of IPPC’s “My Pest Page” has been given. The interface is easy to learn

and use. Plots of forecasted vs. observed can be generated quickly.• Some performance statistics between MtnRT and the NWS NDFD were given.

– In general, MtnRT responded better to day-to-day variation in temperature than NDFD, for Tmin forecasts for frost during the past five seasons (2009-2014).

– The latest maps of high resolution NDFD forecasts showed some apparent non-meteorological bias differences between WFO forecast areas. MtnRT has not shown such a pattern of bias differences.

• MtnRT performs best on temperature, RH and rain. Wind forecasting is more difficult and more complex, especially on slopes, hills.

• Incorporating PRISM data can improve the forecast of MtnRT Tmin • We showed an example of performance trends of estimated (V2 method) versus

observed temperature inside and outside the canopy in a hop yard at the location of the virtual weather station.

• Many thanks to Dr’s Leonard Coop, David Gent, and Christopher Daly for their inputs, and Carla Thomas for reviewing this presentation.

Thank You!!Alan Fox, DirectorFox Weather, LLC726 13th Street, Suite A Fortuna, CA 95540 Office 707 725-8013 Mobile 805 469-1368 Fax 707 725-9380 e-mail alan@foxweather.com

IPPC Contact: Dr. Leonard CoopAssis. Prof. (Research), Dept Botany & Plant PathologyAssoc. Director, Integrated Plant Protection Center2040 CordleyOregon State UniversityCorvallis OR 97331-2907Phone 541-737-5523http://uspest.org/coopl