Hou D.,Environmental Modeling Center NOAA |
Charles M.,Environmental Modeling Center NOAA |
Charles M.,SAIC |
Charles M.,Climate Prediction Center NOAA |
And 13 more authors.
Journal of Hydrometeorology | Year: 2014
Two widely used precipitation analyses are the Climate Prediction Center (CPC) unified global daily gauge analysis and Stage IV analysis based on quantitative precipitation estimate with multisensor observations. The former is based on gauge records with a uniform quality control across the entire domain and thus bears more confidence, but provides only 24-h accumulation at 1/ 8° resolution. The Stage IV dataset, on the other hand, has higher spatial and temporal resolution, but is subject to different methods of quality control and adjustments by different River Forecasting Centers. This article describes a methodology used to generate a new dataset by adjusting the Stage IV 6-h accumulations based on available joint samples of the two analyses to take advantage of both datasets.Asimple linear regressionmodel is applied to the archived historical Stage IV and the CPC datasets after the former is aggregated to the CPC grid and daily accumulation. The aggregated Stage IV analysis is then adjusted based on this linearmodel and then downscaled back to its original resolution. The new dataset, named Climatology-Calibrated Precipitation Analysis (CCPA), retains the spatial and temporal patterns of the Stage IV analysis while having its long-term average and climate probability distribution closer to that of the CPC analysis. The limitation of the methodology at some locations is mainly associated with heavy to extreme precipitation events, which the Stage IV dataset tends to underestimate. CCPA cannot effectively correct this because of the linear regression model and the relative scarcity of heavy precipitation in the training data sample. © 2014 American Meteorological Society.
Mehra A.,Environmental Modeling Center NOAA |
Terrestrial, Atmospheric and Oceanic Sciences | Year: 2010
The Real Time Ocean Forecast System (RTOFS) for the North Atlantic is an ocean forecast system based on the HYbrid Coordinate Ocean Model (HYCOM). HYCOM is the result of a collaborative effort between the University of Miami, the Naval Research Laboratory (NRL), and the Los Alamos National Laboratory (LANL), as part of a multi-institutional HYCOM Consortium for Data-Assimilative Ocean Modeling funded by the National Ocean Partnership Program (NOPP) to develop and evaluate a data-assimilative hybrid isopycnal-sigma-pressure (generalized) coordinate ocean model. This paper describes the RTOFS-Atlantic, an operational real time ocean nowcast/forecast system for the North Atlantic running daily at National Centers for Environmental Prediction (NCEP).
Su X.,Nanjing University |
Yuan H.,Nanjing University |
Yuan H.,Jiangsu Collaborative Innovation Center for Climate Change |
Zhu Y.,Environmental Modeling Center NOAA |
And 3 more authors.
Journal of Geophysical Research: Atmospheres | Year: 2014
The ensemble mean quantitative precipitation forecasts (QPFs) and probabilistic QPFs (PQPFs) from six operational global ensemble prediction systems (EPSs) in The Observing System Research and Predictability Experiment Interactive Grand Global Ensemble (TIGGE) data set are evaluated against the Tropical Rainfall Measuring Mission observations using a series of area-weighted verification metrics during June to August 2008-2012 in the Northern Hemisphere (NH) midlatitude and tropics. Results indicate that generally the European Centre for Medium-Range Weather Forecasts performs best while the Canadian Meteorological Centre (CMC) is relatively good for short-range QPFs and PQPFs at light precipitation thresholds. The overall forecast skill is better in the NH midlatitude than in the NH tropics. QPFs and PQPFs from China Meteorological Administration (CMA) have very little discrimination ability of different observed rain events in the NH tropics. The day +1 QPFs from Japan Meteorological Agency have remarkably large moist biases in the NH tropics, which leads to the discontinuity of forecast performance with the lead times. Performance changes due to the major EPS upgrades during the five summers are also examined using the forecasts from CMA as the reference to eliminate the interannual variation. After the EPS upgrade, CMC improves the PQPF skill at light precipitation threshold while its excessively enlarged ensemble spread increases the overall QPF and PQPF errors. Key Points The model upgrade in EPS cannot always guarantee forecast skill improvements The enlarged ensemble spread of CMC after the upgrade increases the QPF errors The day +1 QPFs from JMA have unusually large moist biases in the NH tropics ©2014. The Authors.