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![]() October 1, 2017.Based on preliminary analysis, the average annual temperature for the contiguous U.S. The four year project was awarded by CA-DWR and officially kicked off Water Management Plan, Bay Area Regional Climate Change Preparedness Program. Submitted to a grant solicitation for CA-DWR Proposition 84 under the Bay Area Integrated Regional In 2015 a proposal for a regional implementation ofĪQPI in the nine county region surrounding the San Francisco Bay area was The AQPI concept of using state-of-the-art radars to improve monitoringĪnd short-term precipitation forecasts is a natural extension of the HMTĪnd EFREP programs. Weather forecast models builds on lessons learned in NOAA's HMT. ![]() Statewide deployment of observing systems and suite of highly detailed Precipitation monitoring and prediction, especially for extreme events. Preparedness (EFREP) program, ESRL and CA-DWR are working to improve As part of CA-DWR’s Enhanced Flood Response and Emergency Water Resources (CA-DWR) to address water resource and flood protection System Research Laboratory (ESRL) partnered with the California Department of NOAA's Hydrometeorology Testbed (HMT) program. The seeds of AQPI were sown in the early 2000s with the deployment ofĪdvanced instrumentation and research studies focused on understandingĮxtreme precipitation events in the CA coastal range and Sierra as part of Media Contact: NOAA Public Affairs Officer Chandrasekar (December 2019): Assessment of antecedent moisture condition on flood frequency: An experimental study in Napa River Basin, CA. Cifelli (October 2019): Hybrid Machine Learning Framework for Hydrological Assessment. Lim (August 2019): Modeling Streamflow Enhanced by Precipitation from Atmospheric River Using the NOAA National Water Model: A Case Study of the Russian River Basin for February 2004. Ma (December 2019): A Flexible Bayesian Approach to Bias Correction of Radar-derived Precipitation Estimates over Complex Terrain: Model Design and Initial Verification. Cifelli (March 2019): A multiscale evaluation of multisensor quantitative precipitation estimates in the Russian River Basin. Barnard (September 2020): Effect of Fluvial Discharges and Remote Non-Tidal Residuals on Compound Flood Forecasting in San Francisco Bay. Han (May 2020): An experiment on reservoir representation schemes to improve hydrologic prediction: coupling the national water model with the HEC-ResSim, Hydrol. White (December 2020): Benefits of an Advanced Quantitative Precipitation Information System. Xie (Feburary 2020): A Machine Learning System for Precipitation Estimation Using Satellite and Ground Radar Network Observations. IEEE Transactions on Geoscience and Remote Sensing, 58(3), 1821-1832. ![]() White (March 2020): Improving Operational Radar Rainfall Estimates Using Profiler Observations Over Complex Terrain in Northern California. Cifelli (April 2020): On the uncertainty of high resolution hourly Quantitative Precipitation Estimates in California. Characteristics of long-duration heavy precipitation events along the West Coast of the United States. Cifelli (July 2021): Quantifying the potential of AQPI gap-filling radar network for streamflow simulation through a WRF-Hydro experiment, J. Evaluating Operational and Experimental HRRR Model Forecasts of Atmospheric River Events in California, Weather Forecast., 36(6), 1925-1944. Anderson (January 2021): Assessment of Flood Forecast Products for a Coupled Tributary-Coastal Model. Cifelli, and Pingping Xie (August 2021): Deep learning for bias correction of satellite retrievals of orographic precipitation. On precipitation and coastal Bay inundation from extreme storms–especially moisture-laden Streamflow and soil moisture and a suite of forecast modeling systems to improve lead time Short-term nowcasting (0-1 hours) additional surface measurements of precipitation, The AQPI system consists of improved weather radar data for precipitation estimation and Storm tracking when hazardous weather events come onshore.Īdvanced Quantitative Precipitation Information (AQPI) is a regional projectĪwarded to NOAA and collaborating partners by the California Department of Water Resources. In the San Francisco Bay region can enhance public safety through early warning and ![]() Improved precipitation monitoring and prediction Ranges, where precipitation can be heaviest. What is happening just above the complex landscape of California’s coastal mountain Into Midwest thunderstorms, are often unable to give an accurate picture of Standard weather radars, originally designed to look up ![]() Making decisions regarding public safety, infrastructure operations, and Accurate and timely precipitation information is critical for Operations, flood protection, combined sewer-stormwater systems and emergency When big storms hit California, current technology does not provideįorecasters with the detailed information needed to inform reservoir ![]()
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