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

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.

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

Weather and Forecasting ◽

10.1175/waf973.1 ◽

2007 ◽

Vol 22 (1) ◽

pp. 36-55 ◽

Cited By ~ 39

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.

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

Weather and Forecasting ◽

10.1175/waf-d-19-0182.1 ◽

2020 ◽

Vol 35 (3) ◽

pp. 857-877 ◽

Cited By ~ 1

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.

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

Climate Dynamics ◽

10.1007/s00382-021-05632-z ◽

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.

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GEFS Precipitation Forecasts and the Implications of Statistical Downscaling over the Western United States

Weather and Forecasting ◽

10.1175/waf-d-16-0179.1 ◽

2017 ◽

Vol 32 (3) ◽

pp. 1007-1028 ◽

Cited By ~ 7

Author(s):

Wyndam R. Lewis ◽

W. James Steenburgh ◽

Trevor I. Alcott ◽

Jonathan J. Rutz

Keyword(s):

United States ◽

Statistical Downscaling ◽

Performance Metrics ◽

Western United States ◽

False Alarms ◽

Ensemble Spread ◽

Modeling Systems ◽

Weather Forecasters ◽

Orographic Enhancement ◽

Snow Telemetry

Abstract Contemporary operational medium-range ensemble modeling systems produce quantitative precipitation forecasts (QPFs) that provide guidance for weather forecasters, yet lack sufficient resolution to adequately resolve orographic influences on precipitation. In this study, cool-season (October–March) Global Ensemble Forecast System (GEFS) QPFs are verified using daily (24 h) Snow Telemetry (SNOTEL) observations over the western United States, which tend to be located at upper elevations where the orographic enhancement of precipitation is pronounced. Results indicate widespread dry biases, which reflect the infrequent production of larger 24-h precipitation events (≳22.9 mm in Pacific ranges and ≳10.2 mm in the interior ranges) compared with observed. Performance metrics, such as equitable threat score (ETS), hit rate, and false alarm ratio, generally worsen from the coast toward the interior. Probabilistic QPFs exhibit low reliability, and the ensemble spread captures only ~30% of upper-quartile events at day 5. In an effort to improve QPFs without exacerbating computing demands, statistical downscaling is explored based on high-resolution climatological precipitation analyses from the Parameter-Elevation Regressions on Independent Slopes Model (PRISM), an approach frequently used by operational forecasters. Such downscaling improves model biases, ETSs, and hit rates. However, 47% of downscaled QPFs for upper-quartile events are false alarms at day 1, and the ensemble spread captures only 56% of the upper-quartile events at day 5. These results should help forecasters and hydrologists understand the capabilities and limitations of GEFS forecasts and statistical downscaling over the western United States and other regions of complex terrain.

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Initial Conditions for Convection-Allowing Ensembles over the Conterminous United States

Monthly Weather Review ◽

10.1175/mwr-d-19-0401.1 ◽

2020 ◽

Vol 148 (7) ◽

pp. 2645-2669

Author(s):

Craig S. Schwartz ◽

May Wong ◽

Glen S. Romine ◽

Ryan A. Sobash ◽

Kathryn R. Fossell

Keyword(s):

United States ◽

Ensemble Kalman Filter ◽

Initial Conditions ◽

Limited Area ◽

Grid Spacing ◽

Forecast System ◽

Skill Scores ◽

Ensemble Mean ◽

Horizontal Grid ◽

Better Than

Abstract Five sets of 48-h, 10-member, convection-allowing ensemble (CAE) forecasts with 3-km horizontal grid spacing were systematically evaluated over the conterminous United States with a focus on precipitation across 31 cases. The various CAEs solely differed by their initial condition perturbations (ICPs) and central initial states. CAEs initially centered about deterministic Global Forecast System (GFS) analyses were unequivocally better than those initially centered about ensemble mean analyses produced by a limited-area single-physics, single-dynamics 15-km continuously cycling ensemble Kalman filter (EnKF), strongly suggesting relative superiority of the GFS analyses. Additionally, CAEs with flow-dependent ICPs derived from either the EnKF or multimodel 3-h forecasts from the Short-Range Ensemble Forecast (SREF) system had higher fractions skill scores than CAEs with randomly generated mesoscale ICPs. Conversely, due to insufficient spread, CAEs with EnKF ICPs had worse reliability, discrimination, and dispersion than those with random and SREF ICPs. However, members in the CAE with SREF ICPs undesirably clustered by dynamic core represented in the ICPs, and CAEs with random ICPs had poor spinup characteristics. Collectively, these results indicate that continuously cycled EnKF mean analyses were suboptimal for CAE initialization purposes and suggest that further work to improve limited-area continuously cycling EnKFs over large regional domains is warranted. Additionally, the deleterious aspects of using both multimodel and random ICPs suggest efforts toward improving spread in CAEs with single-physics, single-dynamics, flow-dependent ICPs should continue.

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Establishment and Trends in Persistence of Selected Perennial Cool-Season Grasses in Western United States

Rangeland Ecology & Management ◽

10.1016/j.rama.2018.06.008 ◽

2018 ◽

Vol 71 (6) ◽

pp. 681-690 ◽

Cited By ~ 3

Author(s):

Craig W. Rigby ◽

Kevin B. Jensen ◽

J. Earl Creech ◽

Eric T. Thacker ◽

Blair L. Waldron ◽

...

Keyword(s):

United States ◽

Western United States ◽

Cool Season

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Model Development and Hindcast Simulations of NOAA’s Gulf of Maine Operational Forecast System

Journal of Marine Science and Engineering ◽

10.3390/jmse4040077 ◽

2016 ◽

Vol 4 (4) ◽

pp. 77 ◽

Cited By ~ 5

Author(s):

Zizang Yang ◽

Philip Richardson ◽

Yi Chen ◽

John Kelley ◽

Edward Myers ◽

...

Keyword(s):

Gulf Of Maine ◽

Model Development ◽

Forecast System ◽

Operational Forecast

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Prediction of Summer Maximum and Minimum Temperature over the Central and Western United States: The Roles of Soil Moisture and Sea Surface Temperature

Journal of Climate ◽

10.1175/jcli3665.1 ◽

2006 ◽

Vol 19 (8) ◽

pp. 1407-1421 ◽

Cited By ~ 48

Author(s):

Eric J. Alfaro ◽

Alexander Gershunov ◽

Daniel Cayan

Keyword(s):

United States ◽

Soil Moisture ◽

Short Range ◽

Large Scale ◽

Severity Index ◽

Time Frame ◽

Western United States ◽

Drought Severity ◽

Explanatory Variables ◽

Predictive Skill

Abstract A statistical model based on canonical correlation analysis (CCA) was used to explore climatic associations and predictability of June–August (JJA) maximum and minimum surface air temperatures (Tmax and Tmin) as well as the frequency of Tmax daily extremes (Tmax90) in the central and western United States (west of 90°W). Explanatory variables are monthly and seasonal Pacific Ocean SST (PSST) and the Climate Division Palmer Drought Severity Index (PDSI) during 1950–2001. Although there is a positive correlation between Tmax and Tmin, the two variables exhibit somewhat different patterns and dynamics. Both exhibit their lowest levels of variability in summer, but that of Tmax is greater than Tmin. The predictability of Tmax is mainly associated with local effects related to previous soil moisture conditions at short range (one month to one season), with PSST providing a secondary influence. Predictability of Tmin is more strongly influenced by large-scale (PSST) patterns, with PDSI acting as a short-range predictive influence. For both predictand variables (Tmax and Tmin), the PDSI influence falls off markedly at time leads beyond a few months, but a PSST influence remains for at least two seasons. The maximum predictive skill for JJA Tmin, Tmax, and Tmax90 is from May PSST and PDSI. Importantly, skills evaluated for various seasons and time leads undergo a seasonal cycle that has maximum levels in summer. At the seasonal time frame, summer Tmax prediction skills are greatest in the Midwest, northern and central California, Arizona, and Utah. Similar results were found for Tmax90. In contrast, Tmin skill is spread over most of the western region, except for clusters of low skill in the northern Midwest and southern Montana, Idaho, and northern Arizona.

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Verification of Extratropical Cyclones within the NCEP Operational Models. Part II: The Short-Range Ensemble Forecast System

Weather and Forecasting ◽

10.1175/2009waf2222170.1 ◽

2009 ◽

Vol 24 (5) ◽

pp. 1191-1214 ◽

Cited By ~ 20

Author(s):

Michael E. Charles ◽

Brian A. Colle

Keyword(s):

United States ◽

Short Range ◽

Wrf Model ◽

Ensemble Forecast ◽

Central Pressure ◽

Extratropical Cyclones ◽

Eastern United States ◽

Forecast System ◽

Eastern Canada ◽

Western Atlantic

Abstract This paper verifies the strengths and positions of extratropical cyclones around North America and the adjacent oceans within the Short Range Ensemble Forecast (SREF) system at the National Centers for Environmental Prediction (NCEP) during the 2004–07 cool seasons (October–March). The SREF mean for cyclone position and central pressure has a smaller error than the various subgroups within SREF and the operational North American Mesoscale (NAM) model in many regions on average, but not the operational Global Forecast System (GFS) for many forecast times. Inclusion of six additional Weather Research and Forecasting (WRF) model members into SREF during the 2006–07 cool season did not improve the SREF mean predictions. The SREF has slightly more probabilistic skill over the eastern United States and western Atlantic than the western portions of the domain for cyclone central pressure. The SREF also has slightly greater probabilistic skill than the combined GFS and NAM for central pressure, which is significant at the 90% level for many regions and thresholds. The SREF probabilities are fairly reliable, although the SREF is overconfident at higher probabilities in all regions. The inclusion of WRF did not improve the SREF probabilistic skill. Over the eastern Pacific, eastern Canada, and western Atlantic, the SREF is overdispersed on average, especially early in the forecast, while across the central and eastern United States the SREF is underdispersed later in the forecast. There are relatively large biases in cyclone central pressure within each SREF subgroup. As a result, the best-member diagrams reveal that the SREF members are not equally accurate for the cyclone central pressure and displacement. Two cases are presented to illustrate examples of SREF developing large errors early in the forecast for cyclones over the eastern United States.

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Revisiting Sensitivity to Horizontal Grid Spacing in Convection-Allowing Models over the Central and Eastern United States

Monthly Weather Review ◽

10.1175/mwr-d-19-0115.1 ◽

2019 ◽

Vol 147 (12) ◽

pp. 4411-4435 ◽

Cited By ~ 4

Author(s):

Craig S. Schwartz ◽

Ryan A. Sobash

Keyword(s):

United States ◽

Large Scale ◽

Prediction Models ◽

Initial Conditions ◽

Weather Prediction ◽

Precipitation Forecast ◽

Eastern United States ◽

Grid Spacing ◽

Cool Season ◽

Horizontal Grid

Abstract Hourly accumulated precipitation forecasts from deterministic convection-allowing numerical weather prediction models with 3- and 1-km horizontal grid spacing were evaluated over 497 forecasts between 2010 and 2017 over the central and eastern conterminous United States (CONUS). While precipitation biases varied geographically and seasonally, 1-km model climatologies of precipitation generally aligned better with those observed than 3-km climatologies. Additionally, during the cool season and spring, when large-scale forcing was strong and precipitation entities were large, 1-km forecasts were more skillful than 3-km forecasts, particularly over southern portions of the CONUS where instability was greatest. Conversely, during summertime, when synoptic-scale forcing was weak and precipitation entities were small, 3- and 1-km forecasts had similar skill. These collective results differ substantially from previous work finding 4-km forecasts had comparable springtime precipitation forecast skill as 1- or 2-km forecasts over the central–eastern CONUS. Additional analyses and experiments suggest the greater benefits of 1-km forecasts documented here could be related to higher-quality initial conditions than in prior studies. However, further research is needed to confirm this hypothesis.

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