HAL and little more

DRAFT – Page 2 – May 19, 2015 Hydrometeorology and Arctic Lab Hydrometeorology –Instrumented Study area –MESH model –Some board participation –Convective Initiation (UNSTABLE) –Convective guidance Arctic (and Climate) –Mainly using climate data for studies –Lightning correlations –Fog/ Stratus –Numerical model evaluation –DRI

DRAFT – Page 3 – May 19, 2015 H is for Hydrology R&D of tools supporting hydrological prediction water availability in arid regions –modeling and remote sensing tools –assess soil moisture in context of hydrological cycle. Satellite validation partnerships in campaigns during 2007, 2008, 2009 A significant collaborative effort with NASA, CSA, AAFC, USDA, U of Guelph, U of Sherbrooke occurred in 2010 Additional partnerships with University of Sask’s Global Institute for Water Security and Ag Canada’s NAIS program in 2011 Achieved status as a NASA SMAP (Soil Moisture Active Passive) validation site in 2012

DRAFT – Page 4 – May 19, 2015 Integration of field studies for remote sensing and modeling validation HAL study site – Kenaston/Brightwater Creek 24 sites (EC) 10 x 10 km grid 24 EC precip, soil moisture stations Hourly precip and soil moisture at 3 depths Nested scale design -24 EC sites -16 U of G sites In the headwaters of Brightwater Creek (05HG002) Suitable for modeling and remote sensing validation at multiple scales

DRAFT – Page 5 – May 19, 2015 Collaboration – CanEx-SM10 SMOS validation SMAP pre-launch algorithm development Partners EC, NASA, AAFC, CSA, U of Guelph, U of Sherbrooke Kenaston 40 times series sites+ 20 additional ground truth sites BERMS 20 time series sites + temporary time series sites + additional ground truth sites BERMS

DRAFT – Page 6 – May 19, 2015 KENaston campaign

DRAFT – Page 7 – May 19, 2015 H is for Hydrological prediction Exploring flow guidance system Based on NWP Polling provinces for interest and scope

DRAFT – Page 8 – May 19, 2015 Background: The NWP System “On-line” mode “Off-line” mode “On-line” mode “Off-line” mode Surface observations Upper air observations CaLDAS: Canadian land data assimilation CaPA: Canadian precipitation analysis GEM atmospheric model 4DVar data assimilation CLASS/ISBA WATFLOOD CRHM Cold Regions Hydrological Model

DRAFT – Page 9 – May 19, 2015 Original Proposal

DRAFT – Page 10 – May 19, 2015 Refined Proposal The task force to explore opportunities for better collaboration between EC and P/T Flood Forecasting agencies in the following ways: 1)Develop a requirements document for EC to use as a basis for improving products and services to P/Ts 2)Produce a discussion document regarding how P/Ts can help EC improve its Numerical Weather Prediction (NWP) model. 3)Help write a 2013 Search and Rescue – New Initiatives Fund (SAR-NIF) proposal for additional funding. 4)Encourage the prototyping and implementation of products and services to improve collaboration between P/T flood forecasters and EC.

DRAFT – Page 11 – May 19, 2015 D is between H and A: Drought Research Initiative – Prairie Extremes A joint University-EC collaboration (UManitoba, USask, HAL, S+T) Funded by CFCAS To better understand the processes associated with the precipitation extremes (both wet and dry) and impacts across the Canadian Prairies that occurred in Variety of Datasets Used Gridded temperature and precipitation data sets (CANGRD, CAPA). NCEP-NCAR reanalysis products. Several surface-based data sources maintained by Environment Canada used to examine temperature and precipitation variations, lightning activity and river discharges. Canadian National Fire Database used to characterize lightning-caused fire and associated area burned statistics

DRAFT – Page 12 – May 19, 2015 Moisture extremes and impacts occurring simultaneously over different parts of the region 2010 Gridded Total Precipitation from CAPA Large Fires (> 200 ha) on the Prairies

DRAFT – Page 13 – May 19, Gridded Lightning Activity

DRAFT – Page 14 – May 19, 2015 Surface rainfall and cloud-to-ground lightning relationships in Canada Exploratory Study: Can a predictive capability to estimate convective rainfall using lightning information be developed? Objectives: –Develop relationship between lightning activity and surface rainfall in Canada [rainfall yield] for period April-October –Assess how well the derived rain yields can predict convective precipitation in Canada for the April- October seasons of 2004 and 2010.

DRAFT – Page 15 – May 19, 2015 Surface rainfall and cloud-to-ground lightning relationships in Canada Spatial pattern of rainfall yields across Canada’s ecozones for the period Apr-Oct (units : x10 8 kg fl -1 [kg per flash])

DRAFT – Page 16 – May 19, 2015 Surface rainfall and cloud-to-ground lightning relationships in Canada A broad swath of the middle and northern portions of Canada lie outside of radar coverage. Examined the effect of replacing station-derived rain yields with ecozone-derived rain yields. Prediction uncertainty error = ratio of ecozone MAE to observed precipitation (percentage) A predictive capability to estimate seasonal convective rainfall using lightning information may be feasible in data sparse regions without radar coverage, but the predictions exhibit greater uncertainty in some ecozones than in others.

DRAFT – Page 17 – May 19, 2015