scholarly journals Verification of Surface Temperature Forecasts from the National Digital Forecast Database over the Western United States

2006 ◽  
Vol 21 (5) ◽  
pp. 869-892 ◽  
Author(s):  
David T. Myrick ◽  
John D. Horel
Keyword(s):  
United States ◽  
Surface Temperature ◽  
Winter Season ◽  
Forecast Accuracy ◽  
Horizontal Resolution ◽  
Western United States ◽  
Lead Times ◽  
Advanced Regional Prediction System ◽  
Regional Prediction ◽  
Mean Square Errors

Abstract Experimental gridded forecasts of surface temperature issued by National Weather Service offices in the western United States during the 2003/04 winter season (18 November 2003–29 February 2004) are evaluated relative to surface observations and gridded analyses. The 5-km horizontal resolution gridded forecasts issued at 0000 UTC for forecast lead times at 12-h intervals from 12 to 168 h were obtained from the National Digital Forecast Database (NDFD). Forecast accuracy and skill are determined relative to observations at over 3000 locations archived by MesoWest. Forecast quality is also determined relative to Rapid Update Cycle (RUC) analyses at 20-km resolution that are interpolated to the 5-km NDFD grid as well as objective analyses obtained from the Advanced Regional Prediction System Data Assimilation System that rely upon the MesoWest observations and RUC analyses. For the West as a whole, the experimental temperature forecasts issued at 0000 UTC during the 2003/04 winter season exhibit skill at lead times of 12, 24, 36, and 48 h on the basis of several verification approaches. Subgrid-scale temperature variations and observational and analysis errors undoubtedly contribute some uncertainty regarding these results. Even though the “true” values appropriate to evaluate the forecast values on the NDFD grid are unknown, it is estimated that the root-mean-square errors of the NDFD temperature forecasts are on the order of 3°C at lead times shorter than 48 h and greater than 4°C at lead times longer than 120 h. However, such estimates are derived from only a small fraction of the NDFD grid boxes. Incremental improvements in forecast accuracy as a result of forecaster adjustments to the 0000 UTC temperature grids from 144- to 24-h lead times are estimated to be on the order of 13%.

scholarly journals Sensitivity of Surface Analyses over the Western United States to RAWS Observations

2008 ◽  
Vol 23 (1) ◽  
pp. 145-158 ◽  
Author(s):  
David T. Myrick ◽  
John D. Horel
Keyword(s):  
United States ◽  
Wind Speed ◽  
Winter Season ◽  
Western United States ◽  
Cold Pool ◽  
Federal State ◽  
Advanced Regional Prediction System ◽  
Average Analysis ◽  
Regional Prediction ◽  
The Impact

Abstract Federal, state, and other wildland resource management agencies contribute to the collection of weather observations from over 1000 Remote Automated Weather Stations (RAWS) in the western United States. The impact of RAWS observations on surface objective analyses during the 2003/04 winter season was assessed using the Advanced Regional Prediction System (ARPS) Data Assimilation System (ADAS). A set of control analyses was created each day at 0000 and 1200 UTC using the Rapid Update Cycle (RUC) analyses as the background fields and assimilating approximately 3000 surface observations from MesoWest. Another set of analyses was generated by withholding all of the RAWS observations available at each time while 10 additional sets of analyses were created by randomly withholding comparable numbers of observations obtained from all sources. Random withholding of observations from the analyses provides a baseline estimate of the analysis quality. Relative to this baseline, removing the RAWS observations degrades temperature (wind speed) analyses by an additional 0.5°C (0.9 m s−1) when evaluated in terms of rmse over the entire season. RAWS temperature observations adjust the RUC background the most during the early morning hours and during winter season cold pool events in the western United States while wind speed observations have a greater impact during active weather periods. The average analysis area influenced by at least 1.0°C (2.5°C) by withholding each RAWS observation is on the order of 600 km2 (100 km2). The spatial influence of randomly withheld observations is much less.

scholarly journals Comparison of Lightning Forecasts from the High-Resolution Rapid Refresh Model to Geostationary Lightning Mapper Observations

2020 ◽  
Vol 35 (2) ◽  
pp. 401-416 ◽  
Author(s):  
Brian K. Blaylock ◽  
John D. Horel
Keyword(s):  
United States ◽  
High Resolution ◽  
Forecast Accuracy ◽  
Skill Score ◽  
Western United States ◽  
Lead Times ◽  
Convective Storms ◽  
Northern Mexico ◽  
Early Evening ◽  
Broad Region

Abstract The ability of the High-Resolution Rapid Refresh (HRRR) model to forecast the location of convective storms is of interest for a variety of applications. Since lightning is often present with intense convection, lightning observations from the Geostationary Lightning Mapper (GLM) on GOES-East are used to evaluate the performance of the HRRR lightning forecasts from May through September during 2018 and 2019. Model skill is presented in terms of the fractions skill score (FSS) evaluated within circular neighborhoods with radial distances from 30 to 240 km. Case studies of individual events illustrate that the HRRR lightning forecasts FSS varies from storm to storm. Mean FSS is summarized for the months with peak lightning activity (June–August) for the west, central, and east United States. Our results suggest that forecasters should use HRRR lightning forecasts to indicate general tendencies for the occurrence, region, and timing of thunderstorms in a broad region rather than expect high forecast accuracy for lightning locally. For example, when FSS is evaluated within small neighborhoods (30-km radius), mean FSS drops sharply after the first two hours of model integration in all regions and during all hours of the day. However, when evaluated within larger neighborhoods (60-km radius and larger), FSS in the western United States and northern Mexico remains high for all lead times in the late afternoon and early evening. This result is likely due to the model capturing the tendency for convection to break out over higher terrain during those hours.

scholarly journals Assessment of the Sea Surface Temperature Predictability Based on Multimodel Hindcasts

2019 ◽  
Vol 34 (6) ◽  
pp. 1965-1977 ◽  
Author(s):  
Shouwen Zhang ◽  
Hua Jiang ◽  
Hui Wang
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Forecast Accuracy ◽  
Skill Score ◽  
Point Of View ◽  
Sea Surface ◽  
Lead Times ◽  
Spring Predictability Barrier ◽  
Multimodel Ensembles ◽  
The Tropics

Abstract Based on historical forecasts of four individual forecasting systems, we conducted multimodel ensembles (MME) to predict the sea surface temperature anomaly (SSTA) variability and assessed these methods from a deterministic and probabilistic point of view. To investigate the advantages and drawbacks of different deterministic MME methods, we used simple averaged MME with equal weighs (SCM) and the stepwise pattern projection method (SPPM). We measured the probabilistic forecast accuracy by Brier skill score (BSS) combined with its two components: reliability (Brel) and resolution (Bres). The results indicated that SCM showed a high predictability in the tropical Pacific Ocean, with a correlation exceeding 0.8 with a 6-month lead time. In general, the SCM outperformed the SPPM in the tropics, while the SPPM tend to show some positive effect on the correction when at long lead times. Corrections occurred for the spring predictability barrier of ENSO, in particular for improvements when the correlation was low or the RMSE was large using the SCM method. These qualitative results are not susceptible to the selection of the hindcast periods, it is as a rule rather by chance of these individual systems. Performance of our probabilistic MME was better than the Climate Forecast System version2 (CFSv2) forecasts in forecasting COLD, NEUTRAL, and WARM SSTA categories for most regions, mainly due to the contribution of Brel, indicating more adequate ensemble construction strategies of the MME system superior to the CFSv2.

scholarly journals Spatiotemporal Characteristics of Snowpack Density in the Mountainous Regions of the Western United States

2008 ◽  
Vol 9 (6) ◽  
pp. 1416-1426 ◽  
Author(s):  
Naoki Mizukami ◽  
Sanja Perica
Keyword(s):  
United States ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Winter Season ◽  
Western United States ◽  
Densification Rate ◽  
Snow Density ◽  
Mountainous Regions ◽  
Spatiotemporal Characteristics ◽  
Snow Water

Abstract Snow density is calculated as a ratio of snow water equivalent to snow depth. Until the late 1990s, there were no continuous simultaneous measurements of snow water equivalent and snow depth covering large areas. Because of that, spatiotemporal characteristics of snowpack density could not be well described. Since then, the Natural Resources Conservation Service (NRCS) has been collecting both types of data daily throughout the winter season at snowpack telemetry (SNOTEL) sites located in the mountainous areas of the western United States. This new dataset provided an opportunity to examine the spatiotemporal characteristics of snowpack density. The analysis of approximately seven years of data showed that at a given location and throughout the winter season, year-to-year snowpack density changes are significantly smaller than corresponding snow depth and snow water equivalent changes. As a result, reliable climatological estimates of snow density could be obtained from relatively short records. Snow density magnitudes and densification rates (i.e., rates at which snow densities change in time) were found to be location dependent. During early and midwinter, the densification rate is correlated with density. Starting in early or mid-March, however, snowpack density increases by approximately 2.0 kg m−3 day−1 regardless of location. Cluster analysis was used to obtain qualitative information on spatial patterns of snowpack density and densification rates. Four clusters were identified, each with a distinct density magnitude and densification rate. The most significant physiographic factor that discriminates between clusters was proximity to a large water body. Within individual mountain ranges, snowpack density characteristics were primarily dependent on elevation.

Evaluation of Surface Sensible Weather Forecasts by the WRF and the Eta Models over the Western United States

2005 ◽  
Vol 20 (5) ◽  
pp. 812-821 ◽  
Author(s):  
William Y. Y. Cheng ◽  
W. James Steenburgh
Keyword(s):  
United States ◽  
Land Surface ◽  
Warm Season ◽  
Cooperative Networks ◽  
Western United States ◽  
Surface Model ◽  
Surface Boundary ◽  
Weather Forecasts ◽  
Soil Model ◽  
Regional Prediction

Abstract An evaluation of the surface sensible weather forecasts using high-density observations provided by the MesoWest cooperative networks illustrates the performance characteristics of the Cooperative Institute for Regional Prediction (CIRP) Weather Research and Forecast (WRF) and the Eta Models over the western United States during the 2003 warm season (June–August). In general, CIRP WRF produced larger 2-m temperature and dewpoint mean absolute and bias errors (MAEs and BEs, respectively) than the Eta. CIRP WRF overpredicted the 10-m wind speed, whereas the Eta exhibited an underprediction with a comparable error magnitude to CIRP WRF. Tests using the Oregon State University (OSU) Land Surface Model (LSM) in CIRP WRF, instead of a simpler slab-soil model, suggest that using a more sophisticated LSM offers no overall advantage in reducing WRF BEs and MAEs for the aforementioned surface variables. Improvements in the initialization of soil temperature in the slab-soil model, however, did reduce the temperature bias in CIRP WRF. These results suggest that improvements in LSM initialization may be as or more important than improvements in LSM physics. A concerted effort must be undertaken to improve both the LSM initialization and parameterization of coupled land surface–boundary layer processes to produce more accurate surface sensible weather forecasts.

Local Adjustment of the Background Error Correlation for Surface Analyses over Complex Terrain

2005 ◽  
Vol 20 (2) ◽  
pp. 149-160 ◽  
Author(s):  
David T. Myrick ◽  
John D. Horel ◽  
Steven M. Lazarus
Keyword(s):  
Surface Temperature ◽  
Correlation Matrix ◽  
Analytic Solution ◽  
Weather Conditions ◽  
Optimal Interpolation ◽  
Unbiased Estimation ◽  
Background Error ◽  
Advanced Regional Prediction System ◽  
Regional Prediction ◽  
Error Correlation

Abstract The terrain between grid points is used to modify locally the background error correlation matrix in an objective analysis system. This modification helps to reduce the influence across mountain barriers of corrections to the background field that are derived from surface observations. This change to the background error correlation matrix is tested using an analytic case of surface temperature that encapsulates the significant features of nocturnal radiation inversions in mountain basins, which can be difficult to analyze because of locally sharp gradients in temperature. Bratseth successive corrections, optimal interpolation, and three-dimensional variational approaches are shown to yield exactly the same surface temperature analysis. Adding the intervening terrain term to the Bratseth approach led to solutions that match more closely the specified analytic solution. In addition, the convergence of the Bratseth solutions to the best linear unbiased estimation of the analytic solution is faster. The intervening terrain term was evaluated in objective analyses over the western United States derived from a modified version of the Advanced Regional Prediction System Data Assimilation System. Local adjustment of the background error correlation matrix led to improved surface temperature analyses by limiting the influence of observations in mountain valleys that may differ from the weather conditions present in adjacent valleys.

scholarly journals Predictive Contributions of Snowmelt and Rainfall to Streamflow Variations in the Western United States

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Xiaohui Zheng ◽  
Qiguang Wang ◽  
Lihua Zhou ◽  
Qing Sun ◽  
Qi Li
Keyword(s):  
United States ◽  
Pacific Northwest ◽  
Climate Models ◽  
Coastal Areas ◽  
Snow Accumulation ◽  
Western United States ◽  
Lead Times ◽  
Wet Season ◽  
Mountain Areas ◽  
Southeastern Pacific

This study used long-term in situ rainfall, snow, and streamflow data to explore the predictive contributions of snowmelt and rainfall to streamflow in six watersheds over the Western United States. Analysis showed that peak snow accumulation, snow-free day, and snowmelt slope all had strong correlation with peak streamflow, particularly in inland basins. Further analysis revealed that the variation of snow accumulation anomaly had strong lead correlation with the variation of streamflow anomaly. Over the entire Western United States, inner mountain areas had lead times of 4–10 pentads. However, in coastal areas, nearly all sites had lead times of less than one pentad. The relative contributions of rainfall and snowmelt to streamflow in different watersheds were calculated based on the snow lead time. The geographic distribution of annual relative contributions revealed that interior areas were dominated by snowmelt contribution, whereas the rainfall contribution dominated coastal areas. In the wet season, the snowmelt contribution increased in the western Pacific Northwest, whereas the rainfall contribution increased in the southeastern Pacific Northwest, southern Upper Colorado, and northern Rio Grande regions. The derived results demonstrated the predictive contributions of snowmelt and rainfall to streamflow. These findings could be considered a reference both for seasonal predictions of streamflow and for prevention of hydrological disasters. Furthermore, they will be helpful in the evaluation and improvement of hydrological and climate models.

Sign in / Sign up

Export Citation Format

Share Document