NPSR December 7-9, 2015 WR REPORT 8 Dec 2015 Andy Edman

What are the biggest challenges your region/center faces? Key thought: Communities and their infrastructures adopt to their climatic norms! How do we identify significant events/impacts that –deviate from these climatic norms –identify these events well in advance –and with skill that communities trust enough to take appropriate action? Examples: 1 inch of snow is a big deal in Dallas (1.5 inches per year), not at Alta, Utah (500 inches) 1 inch of rain is a big deal in Las Vegas (5 inches a year), not in Seattle (38 inches a year), also not in western Washington - Forks WA (120 inches) 1 inch of snow that freezes to the road during rush hour in a major city is a big deal, not in rural areas 15 foot marine waves big deal along east coast, typical for PACNW winter storm

What are the biggest challenges your region/center faces? Improve the internal model based forecast/DSS processes –Verification: Use verification to make informed decisions –Better Impact Information: use of coupled models/new physics –Calibration/Probabilities/ model post processing to identify impacts: Using good science to overcome human biases –Training: Forecasters expected to use models intelligently, but forecaster knowledge limited about how models perform for impacts –Cultural change on how we use models: Using model tools/new techniques to tease information out of models versus just looking at model data –Workload to produce stuff: Less grid editing, more forecast interpretation

WR Science Technology Infusion Division (STID) Science Projects to improve DSS messaging Andy Edman and Mike Staudenmaier NOAA/NWS Western Region Headquarters, Salt Lake City, UT r: Bias : RM SE: r: Bias : RM SE: r: Bias : RM SE: r: Bias : RM SE: Figure 2.Scatter plots of GFS QPF vs QPE at forecast hour 48 for Salt Lake City International Airport (elev. 4626’) and Snowbird SNOTEL (elev. 9640’). The advantage of downscaling in the western portion of the United States is the ability to resolve the precipitation distribution forced by orography. Because of the large differences in precipitation climatology, verifying against individual locations was desirable rather than on a large scale that may mask the benefits of the higher resolution forecast. 73 Ensemble Situational Awareness Table – Context: what is significant? – Confidence: how likely is it? – Atmospheric Rivers and Inland Moisture Penetration – Focus of graduate work and continuing research – Collaboration with CW3E (Marty’s group at Scripps; e.g., CalWater 2015) Flash Flood Scale Hydrologic Modeling – Use new WRF-Hydro framework – Supports vision of National Water Center – Gridded output (not tied to gauged locations) – Investigating short-term ensembles – Significant advancement in NWS hydrologic modeling capabilities NWS QPE HRRR QPF WR Heat Impacts Level (HIL) – Framework to put heat into climatological context – No forecaster workload, high resolution forecast to support partners unique IDSS needs – Experimental FY15 – Forecast Confidence s All projects documented on google site Forecast Confidence tools project WR Road Weather Project – Implementation of METRo model over WR and now much of CR as well (not getting into road weather business) Verification Server - Verification of WR offices model verification Grid Image Maker - Regional Version of the Grid Image Maker. Used to create images for Weather Stories and Social Media GFS13 comparison webpage - Used during the GFS13 30-day evaluation period to compare 13 km GFS with 27 km GFS

FY15 WR Highlights Western Region STI – Accomplishments Science Projects: See attached DSS and Leadership: WR DSS Roadshow, WR LIFT Improving Science and DSS in WR: – Forecast Confidence s, – WR Science Series, – ROC support, – Atmospheric River Events, – CALWater, Mesowest National Tech Projects: – iNWS & WR Situational Awareness Page going national, – Smart Init team National Science Projects: – HRRR and 13km GFS -- Training and AWIPS data sets – GFS 13 side by side page – Blend verification page (with CR) FY16 Key Activities and Planned Milestones – Moving forward on science/service projects – WR Office Visits, SOO-DOH projects, new SOO mentoring – Travel Cap permitting – new SOO orientation STSD SRH

Does the current production suite and products adequately help you address those challenges? Key thought: No -- we still treat models as if the direct output has the greatest value rather than using post-processing, calibration, and verification to glean the critical impact information required.

Is the current amount of available guidance too much, too little, or the right amount? Key thought: No -- it is not the amount of guidance that matters but it is the value and relevance of what is being produced.

What do you need in terms of models or products to meet your challenges in the next 1- 2 years? – Model suite Need to simplify model suite and start to place more focus on future – FACETS, Impacts, UMAC recommendations – HRRR Ensembles, Reforecast – Tom Hamill and RFC HEFS QPE Better Impact Information: use of coupled models/new physics – NWPS, Smoke, Metro, RFC Models Calibration/Probabilities/Model Post processing to identify impacts: Using good science to overcome human biases – WR Ensemble page, OAR HRRR Ensemble project, FACETS, SPC Calibrated Severe, WPC probability, RFC HEFS QPE – Verification: Use verification to make informed decisions – RTMA – MDL Side by Side viewer Focus on sensible weather/impact fields we forecast – NBM just starting Case study versus statistics – MAG – just starting – Training: Forecasters expected to use models intelligently, but forecaster knowledge limited about how models perform for impacts HRRR and GFS-13 was last effort – not much has happened since! – Need a new focus on what training provides – impacts to sensible weather versus model technical geek stuff – Cultural change on how we use models: Using model tools/new techniques to tease information out of models versus just looking at model data – WR Ensemble page, WR Forecast Confidence, OAR/SPC/NSSL/AWC SREF experiments – Workload to produce stuff: Less grid editing, more forecast interpretation – WR verification project (last 10 years) – Blend verification page (with CR) – NBM – coming with RTMA

What do you need in terms of models or products to meet your challenges in the next 1- 2 years? Key Recommendation Almost all of the efforts on the previous page were bottom up innovation or efforts! These efforts needs to become part of the mainstream model development and forecaster support decision process

What do you need in terms of models or products to meet your challenges in the next 1- 2 years? Key Recommendations: RTMA/URMA – Supports verification, training, making model decisions, real time bias correction – Need to continue twice a year upgrades Baby steps toward new vision -- FACETS – Enhance HRRR Framework to support FACETS -- HRRRE Model Post Processing – Calibrated Probabilities -- reforecast project/RFC HEFS QPE – Adopt emerging model tools to identify impacts Training & Dissemination – Short modules needed when models are released focus on impacts to field we forecast! – Tied to verification effort – There should be an expectation of a short minute module for every major upgrade – Fix the darn dissemination issues! Coupled Models – Provide science based impacts -- SWAN to NGPS -- Water Center -- Metro could be next

What do you envision your model/product needs to be in the longer term? Key recommendations: A change in attitude in how we view the model production suite from running the model to provide skillful forecast information. This is the decade of convective resolving models – getting energy transfers associated with convection correct in the models, will improve all forecast at all time scales Model services more directly support our emerging services – providing skillful forecasts that communities trust enough to take appropriate action – WRN, DSS, FACETS UMAC recommendations! –Do we have the correct suite of models in an era of convective resolving models? –Are we getting ready for the FACETS and Water Center era of services? –The HRRR framework was a game changer – why not embrace and enhance this! –The new Global (NGPS) has the same potential. –Jettison the rest -- to focus on model post processing, coupled models, RTMA, Water Center. Verification –Use verification to ascertain model skill for significant events –Use verification to make informed model development decisions –Verification will help inform the forecaster on the true impact of model enhancements –Verification is critical for effective post processing –Creative suggestion: Should model verification (post processing/impact based) be conducted by external groups – possibly through a CSTAR grant or ?? Training –Forecaster need to understand skill – to determine when a model forecast is trustful