Evaluation of Recent NCEP Operational Model Upgrades for Cool-Season Precipitation Forecasting over the Western Conterminous United States

2020 ◽  
Vol 35 (3) ◽  
pp. 857-877 ◽  
Author(s):  
Marcel Caron ◽  
W. James Steenburgh
Keyword(s):  
United States ◽  
High Resolution ◽  
Model Performance ◽  
Cool Season ◽  
Forecast System ◽  
Operational Model ◽  
Precipitation Forecasting ◽  
Modeling Systems ◽  
Forecasting System ◽  
Terrain Representation

Abstract In August 2018 and June 2019, NCEP upgraded the operational versions of the High-Resolution Rapid Refresh (HRRR) and Global Forecast System (GFS), respectively. To inform forecasters and model developers about changes in the capabilities and biases of these modeling systems over the western conterminous United States (CONUS), we validate and compare precipitation forecasts produced by the experimental, preoperational HRRRv3 and GFSv15.0 with the then operational HRRRv2 and GFSv14 during the 2017/18 October–March cool season. We also compare the GFSv14 and GFSv15.0 with the operational, high-resolution configuration of the ECMWF Integrated Forecasting System (HRES). We validate using observations from Automated Surface and Weather Observing System (ASOS/AWOS) stations, which are located primarily in the lowlands, and observations from Snowpack Telemetry (SNOTEL) stations, which are located primarily in the uplands. Changes in bias and skill from HRRRv2 to HRRRv3 are small, with HRRRv3 exhibiting slightly higher (but statistically indistinguishable at a 95% confidence level) equitable threat scores. The GFSv14, GFSv15.0, and HRES all exhibit a wet bias at lower elevations and neutral or dry bias at upper elevations, reflecting insufficient terrain representation. GFSv15.0 performance is comparable to GFSv14 at day 1 and superior at day 3, but lags HRES. These results establish a baseline for current operational HRRR and GFS precipitation capabilities over the western CONUS and are consistent with steady or improving NCEP model performance.

scholarly journals Validation of Mountain Precipitation Forecasts from the Convection-Permitting NCAR Ensemble and Operational Forecast Systems over the Western United States

2018 ◽  
Vol 33 (3) ◽  
pp. 739-765 ◽  
Author(s):  
Thomas M. Gowan ◽  
W. James Steenburgh ◽  
Craig S. Schwartz
Keyword(s):  
United States ◽  
Short Range ◽  
Model Development ◽  
Western United States ◽  
Individual Member ◽  
Cool Season ◽  
Forecast System ◽  
Operational Forecast ◽  
Modeling Systems ◽  
Horizontal Grid

Abstract Convection-permitting ensembles can capture the large spatial variability and quantify the inherent uncertainty of precipitation in areas of complex terrain; however, such systems remain largely untested over the western United States. In this study, we assess the capabilities of deterministic and probabilistic cool-season quantitative precipitation forecasts (QPFs) produced by the 10-member, convection-permitting (3-km horizontal grid spacing) NCAR Ensemble using observations collected by SNOTEL stations at mountain locations across the western United States and precipitation analyses from PRISM. We also examine the performance of operational forecast systems run by NCEP including the High Resolution Rapid Refresh (HRRR) model, the NAM forecast system with a 3-km continental United States (CONUS) nest, GFS, and the Short-Range Ensemble Forecast system (SREF). Overall, we find that higher-resolution models, such as the HRRR, NAM-3km CONUS nest, and an individual member of the NCAR Ensemble, are more deterministically skillful than coarser models, especially over the narrow interior ranges of the western United States, likely because they better resolve topography and thus better simulate orographic precipitation. The 10-member NCAR Ensemble is also more probabilistically skillful than 13-member subensembles composed of each SREF dynamical core, but less probabilistically skillful than the full 26-member SREF, as a result of insufficient spread. These results should help guide future short-range model development and inform forecasters about the capabilities and limitations of several widely used deterministic and probabilistic modeling systems over the western United States.

scholarly journals Evaluation of a Mesoscale Short-Range Ensemble Forecast System over the Northeast United States

2007 ◽  
Vol 22 (1) ◽  
pp. 36-55 ◽  
Author(s):  
Matthew S. Jones ◽  
Brian A. Colle ◽  
Jeffrey S. Tongue
Keyword(s):  
United States ◽  
Wind Speed ◽  
Sea Level ◽  
Short Range ◽  
Warm Season ◽  
Ensemble Forecast ◽  
Cool Season ◽  
Forecast System ◽  
Sea Level Pressure ◽  
Level Pressure

Abstract A short-range ensemble forecast system was constructed over the northeast United States down to 12-km grid spacing using 18 members from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5). The ensemble consisted of 12 physics members with varying planetary boundary layer schemes and convective parameterizations as well as seven different initial conditions (ICs) [five National Centers for Environmental Prediction (NCEP) Eta-bred members at 2100 UTC and the 0000 UTC NCEP Global Forecast System (GFS) and Eta runs]. The full 18-member ensemble (ALL) was verified at the surface for the warm (May–September 2003) and cool (October 2003–March 2004) seasons. A randomly chosen subset of seven physics (PHS) members at each forecast hour was used to quantitatively compare with the seven IC members. During the warm season, the PHS ensemble predictions for surface temperature and wind speed had more skill than the IC ensemble and a control (shared PHS and IC member) run initialized 12 h later (CTL12). During the cool and warm seasons, a 14-day running-mean bias calibration applied to the ALL ensemble (ALLBC) added 10%–30% more skill for temperature, wind speed, and sea level pressure, with the ALLBC far outperforming the CTL12. For the 24-h precipitation, the PHS ensemble had comparable probabilistic skill to the IC ensemble during the warm season, while the IC subensemble was more skillful during the cool season. All ensemble members had large diurnal surface biases, with ensemble variance approximating ensemble uncertainty only for wind direction. Selection of ICs was also important, because during the cool season the NCEP-bred members introduced large errors into the IC ensemble for sea level pressure, while none of the subensembles (PHS, IC, or ALL) outperformed the GFS–MM5 for sea level pressure.

scholarly journals Recommendations on benchmarks for photochemical grid model applications in China: Part I – PM<sub>2.5</sub> and chemical species

2020 ◽  
Author(s):  
Ling Huang ◽  
Yangjun Wang ◽  
Hehe Zhai ◽  
Shuhui Xue ◽  
Tianyi Zhu ◽  
...  
Keyword(s):  
United States ◽  
Objective Assessment ◽  
Model Performance ◽  
Chemical Species ◽  
The United States ◽  
Regulatory Compliance ◽  
Operational Model ◽  
The Past ◽  
Performance Results ◽  
Statistical Metrics

Abstract. Photochemical grid models (PGMs) are being applied more frequently to address diverse scientific and regulatory compliance associated with deteriorated air quality in China for the past decade. Solid evaluation of model performances guarantees the robustness and reliability of the baseline modelling results, so subsequent applications are built on top of it; thus, model performance evaluation (MPE) is a critical step of any PGM applications. MPE procedures and associated benchmarks have been proposed for PGM applications in the United States and Europe. However, with numerous input data needed, diverse model configurations, and evolution of the model itself, no two PGM applications are exactly the same. Therefore, those MPE benchmarks proposed based on studies outside China may not be appropriate for evaluation of the increasing number of PGM applications in China. Here we follow an established approach as published in previous literatures, to recommend statistical benchmarks for evaluation of simulated particulate matter (PM) concentrations in China. A total of 128 peer-reviewed articles published between 2006 and mid-2019 that applied one of four most frequently used PGMs in China are compiled to summarize operational model performance results. Quantile distributions of common statistical metrics are presented for total PM2.5 and speciated components. Influences of different model configurations, including modelling regions and seasons, spatial resolution of modelling grids, temporal resolution of MPE, etc., on the range of reported statistics are discussed. Benchmarks for four frequently used evaluation metrics are provided for two tiers – “goals” and “criteria”, where “goals” represent the best model performance that a model is currently expected to achieve and “criteria” represent the model performance that the majority (i.e. two thirds) of studies can meet. Our proposed benchmarks are further compared with those developed for United States and Europe. Additional recommendations for MPE practices are also given. Results from this study shall help the ever-growing modelling community in China to have a better objective assessment of how well their simulation results are compared with previous studies and to better demonstrate the credibility and robustness of their PGM applications prior to subsequent regulatory assessments.

MULTI-ORDER HYDROLOGIC POSITION: A HIGH-RESOLUTION DATASET FOR THE CONTERMINOUS UNITED STATES

2018 ◽  
Author(s):  
Kenneth Belitz ◽  
◽  
Richard B. Moore ◽  
T.L. Arnold ◽  
J.B. Sharpe ◽  
...  
Keyword(s):  
United States ◽  
High Resolution ◽  
Resolution Dataset

scholarly journals Cool season precipitation projections for California and the Western United States in NA-CORDEX models

2021 ◽  
Author(s):  
Kelly Mahoney ◽  
James D. Scott ◽  
Michael Alexander ◽  
Rachel McCrary ◽  
Mimi Hughes ◽  
...  
Keyword(s):  
United States ◽  
Climate Models ◽  
Snow Water Equivalent ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Western United States ◽  
Monthly Precipitation ◽  
Wet Season ◽  
Cool Season ◽  
Projected Change

AbstractUnderstanding future precipitation changes is critical for water supply and flood risk applications in the western United States. The North American COordinated Regional Downscaling EXperiment (NA-CORDEX) matrix of global and regional climate models at multiple resolutions (~ 50-km and 25-km grid spacings) is used to evaluate mean monthly precipitation, extreme daily precipitation, and snow water equivalent (SWE) over the western United States, with a sub-regional focus on California. Results indicate significant model spread in mean monthly precipitation in several key water-sensitive areas in both historical and future projections, but suggest model agreement on increasing daily extreme precipitation magnitudes, decreasing seasonal snowpack, and a shortening of the wet season in California in particular. While the beginning and end of the California cool season are projected to dry according to most models, the core of the cool season (December, January, February) shows an overall wetter projected change pattern. Daily cool-season precipitation extremes generally increase for most models, particularly in California in the mid-winter months. Finally, a marked projected decrease in future seasonal SWE is found across all models, accompanied by earlier dates of maximum seasonal SWE, and thus a shortening of the period of snow cover as well. Results are discussed in the context of how the diverse model membership and variable resolutions offered by the NA-CORDEX ensemble can be best leveraged by stakeholders faced with future water planning challenges.

scholarly journals A pan-African medium-range ensemble flood forecast system

2015 ◽  
Vol 19 (8) ◽  
pp. 3365-3385 ◽  
Author(s):  
V. Thiemig ◽  
B. Bisselink ◽  
F. Pappenberger ◽  
J. Thielen
Keyword(s):  
Large Scale ◽  
Practical Implication ◽  
River Basins ◽  
Flood Forecasting ◽  
Small Scale ◽  
Flood Forecast ◽  
Flood Events ◽  
Forecast System ◽  
Pan African ◽  
Forecasting System

Abstract. The African Flood Forecasting System (AFFS) is a probabilistic flood forecast system for medium- to large-scale African river basins, with lead times of up to 15 days. The key components are the hydrological model LISFLOOD, the African GIS database, the meteorological ensemble predictions by the ECMWF (European Centre for Medium-Ranged Weather Forecasts) and critical hydrological thresholds. In this paper, the predictive capability is investigated in a hindcast mode, by reproducing hydrological predictions for the year 2003 when important floods were observed. Results were verified by ground measurements of 36 sub-catchments as well as by reports of various flood archives. Results showed that AFFS detected around 70 % of the reported flood events correctly. In particular, the system showed good performance in predicting riverine flood events of long duration (> 1 week) and large affected areas (> 10 000 km2) well in advance, whereas AFFS showed limitations for small-scale and short duration flood events. The case study for the flood event in March 2003 in the Sabi Basin (Zimbabwe) illustrated the good performance of AFFS in forecasting timing and severity of the floods, gave an example of the clear and concise output products, and showed that the system is capable of producing flood warnings even in ungauged river basins. Hence, from a technical perspective, AFFS shows a large potential as an operational pan-African flood forecasting system, although issues related to the practical implication will still need to be investigated.

scholarly journals Evaluation of a High-Resolution SPI for Monitoring Local Drought Severity

2016 ◽  
Vol 55 (10) ◽  
pp. 2247-2262 ◽  
Author(s):  
Rebecca V. Cumbie-Ward ◽  
Ryan P. Boyles
Keyword(s):  
United States ◽  
High Resolution ◽  
Standardized Precipitation Index ◽  
Stage Iv ◽  
Drought Severity ◽  
Cluster Solution ◽  
Historical Distribution ◽  
Drought Conditions ◽  
Hydrologic Prediction ◽  
Strong Agreement

AbstractA standardized precipitation index (SPI) that uses high-resolution, daily estimates of precipitation from the National Weather Service over the contiguous United States has been developed and is referred to as HRD SPI. There are two different historical distributions computed in the HRD SPI dataset, each with a different combination of normals period (1971–2000 or 1981–2010) and clustering solution of gauge stations. For each historical distribution, the SPI is computed using the NCEP Stage IV and Advanced Hydrologic Prediction Service (AHPS) gridded precipitation datasets for a total of four different HRD SPI products. HRD SPIs are found to correlate strongly with independently produced SPIs over the 10-yr period from 2005 to 2015. The drought-monitoring utility of the HRD SPIs is assessed with case studies of drought in the central and southern United States during 2012 and over the Carolinas during 2007–08. A monthly comparison between HRD SPIs and independently produced SPIs reveals generally strong agreement during both events but weak agreement in areas where radar coverage is poor. For both study regions, HRD SPI is compared with the U.S. Drought Monitor (USDM) to assess the best combination of precipitation input, normals period, and station clustering solution. SPI generated with AHPS precipitation and the 1981–2010 PRISM normals and associated cluster solution is found to best capture the spatial extent and severity of drought conditions indicated by the USDM. This SPI is also able to resolve local variations in drought conditions that are not shown by either the USDM or comparison SPI datasets.

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