Hello, A yearly allocation for the LCRC cluster has been requested with the following updated information: Submitter/PI: Scott Collis Project Name: rainfall Division: EVS Project title: Using NEXRAD radar retrievals to test validation and scale dependance of rainfall and impact on hydrological modelling Associated funding: DoE-BER-ARM Plus DHS Other Systems: Assorted ANL systems (my own 32 core machine). Systems at OLCF used for formal ARM work (open cluster with no quota system..) Science: ARM is moving to a model as a product paradigm. NOAA both already has a very large operational network (NEXRAD) and fine scale model (3km, High Resolution Rapid Refresh, HRRR). This project will perform rainfall rate retrievals from NOAA radar and do a scale analysis comparison to HRRR rainfall fields. Furthermore we wish to extend this in collaboration with the University of Chicago to investigate the reaction of the subsurface rainfall stimulus and see if the same relations we see in nature hold true for that observed in climate models. Finally, we are using precipitation retrievals from near-city radars to investigate the impact of resolution of rainfall retrieval on culvert/streamflow predicted by hydrological models. This will enable an objective answer to the question: How high a resolution rainfall model (Regional model) is high enough? Project description: The project will use the Python-ARM Radar Toolkit (Py-ART) to ingest, correct and retrieve rainfall rates from dual polarimetric NOAA NEXRAD radars. Starting in Oklahoma, Chicago and Portland Maine, and move to investigating other regions. Since time steps (~10 minutes) are independent the methodology is pleasantly parallel. As part of the project we will be installing Py-ART (https://github.com/ARM-DOE/pyart) on Fusion/Blues and will likely need some support. The code is fairly efficient with higher demand components written in C and fortran. It is, however, memory intensive with 2-4GB per job needed. Fortunately this is perfect for the configuration of Blues. We have already mapped the radar problem to Blues using 1000+ cores effectively. Radically reducing the time to retrieve the rainfall rates We will be using this allocation to work through many years (5+) of radar data. Since our last request we have become more efficient at using Blues so the need for core hours has decreased and the amount of work done increased In addition we will be using our allocation for analyzing the results including comparison to gauge data. The team consists of myself and Jonathan Helmus. Industry partnership: Project URL: Current FY Hours Used: undetermined amount New FY Requested allocation: 60000 Q1: 15000 Q2: 15000 Q3: 20000 Q4: 10000 Justification: Storage requirements: None, we have paid for a large storage system Thank You, The LCRC Accounts System