scholarly journals Evaluation of Statistical Downscaling of North American Multimodel Ensemble Forecasts over the Western United States

2017 ◽  
Vol 32 (1) ◽  
pp. 327-341 ◽  
Author(s):  
Renaud Barbero ◽  
John T. Abatzoglou ◽  
Katherine C. Hegewisch
Keyword(s):  
United States ◽  
Statistical Downscaling ◽  
North American ◽  
Southern Oscillation ◽  
Western United States ◽  
Seasonal Temperature ◽  
Multimodel Ensemble ◽  
Temperature And Precipitation ◽  
Spatial Disaggregation ◽  
North American Multimodel Ensemble

AbstractThe skill of two statistical downscaled seasonal temperature and precipitation forecasts from the North American Multimodel Ensemble (NMME) was evaluated across the western United States at spatial scales relevant to local decision-making. Both statistical downscaling approaches, spatial disaggregation (SD) and bias correction spatial disaggregation (BCSD), exhibited similar correlative skill measures; however, the BCSD method showed superior tercile-based skill measures since it corrects for variance deflation in NMME ensemble averages. Geographic and seasonal variations in downscaled forecast skill revealed patterns across the complex topography of the western United States not evident using coarse-scale skill assessments, particularly in regions subject to inversions and variability in orographic precipitation ratios. Similarly, differences in the skill of cool-season temperature and precipitation forecasts issued when the fall El Niño–Southern Oscillation (ENSO) signal was strong versus ENSO-neutral years were evident across topographic gradients in the northwestern United States.

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

2017 ◽  
Vol 32 (3) ◽  
pp. 1007-1028 ◽  
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.

More reliable coastal SST forecasts from the North American multimodel ensemble

2017 ◽  
Vol 53 (12) ◽  
pp. 7153-7168 ◽  
Author(s):  
G. Hervieux ◽  
M. A. Alexander ◽  
C. A. Stock ◽  
M. G. Jacox ◽  
K. Pegion ◽  
...  
Keyword(s):  
North American ◽  
Multimodel Ensemble ◽  
The North ◽  
North American Multimodel Ensemble

Do Statistical Pattern Corrections Improve Seasonal Climate Predictions in the North American Multimodel Ensemble Models?

2017 ◽  
Vol 30 (20) ◽  
pp. 8335-8355 ◽  
Author(s):  
Anthony G. Barnston ◽  
Michael K. Tippett
Keyword(s):  
North American ◽  
Large Scale ◽  
Climate Model ◽  
Principal Component ◽  
Eastern Africa ◽  
Multimodel Ensemble ◽  
Anomaly Correlation ◽  
The North ◽  
The Individual ◽  
North American Multimodel Ensemble

Abstract Canonical correlation analysis (CCA)-based statistical corrections are applied to seasonal mean precipitation and temperature hindcasts of the individual models from the North American Multimodel Ensemble project to correct biases in the positions and amplitudes of the predicted large-scale anomaly patterns. Corrections are applied in 15 individual regions and then merged into globally corrected forecasts. The CCA correction dramatically improves the RMS error skill score, demonstrating that model predictions contain correctable systematic biases in mean and amplitude. However, the corrections do not materially improve the anomaly correlation skills of the individual models for most regions, seasons, and lead times, with the exception of October–December precipitation in Indonesia and eastern Africa. Models with lower uncorrected correlation skill tend to benefit more from the correction, suggesting that their lower skills may be due to correctable systematic errors. Unexpectedly, corrections for the globe as a single region tend to improve the anomaly correlation at least as much as the merged corrections to the individual regions for temperature, and more so for precipitation, perhaps due to better noise filtering. The lack of overall improvement in correlation may imply relatively mild errors in large-scale anomaly patterns. Alternatively, there may be such errors, but the period of record is too short to identify them effectively but long enough to find local biases in mean and amplitude. Therefore, statistical correction methods treating individual locations (e.g., multiple regression or principal component regression) may be recommended for today’s coupled climate model forecasts. The findings highlight that the performance of statistical postprocessing can be grossly overestimated without thorough cross validation or evaluation on independent data.

scholarly journals Statistical Downscaling Multimodel Forecasts for Seasonal Precipitation and Surface Temperature over the Southeastern United States

2014 ◽  
Vol 27 (22) ◽  
pp. 8384-8411 ◽  
Author(s):  
Di Tian ◽  
Christopher J. Martinez ◽  
Wendy D. Graham ◽  
Syewoon Hwang
Keyword(s):  
United States ◽  
Bias Correction ◽  
Polynomial Regression ◽  
Southeastern United States ◽  
Mapping Technique ◽  
Lead Times ◽  
Equal Weight ◽  
Seasonal Precipitation ◽  
Multimodel Ensemble ◽  
Spatial Disaggregation

Abstract This study compared two types of approaches to downscale seasonal precipitation (P) and 2-m air temperature (T2M) forecasts from the North American Multimodel Ensemble (NMME) over the states of Alabama, Georgia, and Florida in the southeastern United States (SEUS). Each NMME model forecast was evaluated. Two multimodel ensemble (MME) schemes were tested by assigning equal weight to all forecast members (SuperEns) or by assigning equal weights to each model’s ensemble mean (MeanEns). One type of downscaling approach used was a model output statistics (MOS) method, which was based on direct spatial disaggregation and bias correction of the NMME P and T2M forecasts using the quantile mapping technique [spatial disaggregation with bias correction (SDBC)]. The other type of approach used was a perfect prognosis (PP) approach using nonparametric locally weighted polynomial regression (LWPR) models, which used the NMME forecasts of Niño-3.4 sea surface temperatures (SSTs) to predict local-scale P and T2M. Both SDBC and LWPR downscaled P showed skill in winter but no skill or limited skill in summer at all lead times for all NMME models. The SDBC downscaled T2M were skillful only for the Climate Forecast System, version 2 (CFSv2), model even at far lead times, whereas the LWPR downscaled T2M showed limited skill or no skill for all NMME models. In many cases, the LWPR method showed significantly higher skill than the SDBC. After bias correction, the SuperEns mostly showed higher skill than the MeanEns and most of the single models, but its skill did not outperform the best single model.

The North American Multimodel Ensemble: Phase-1 Seasonal-to-Interannual Prediction; Phase-2 toward Developing Intraseasonal Prediction

2014 ◽  
Vol 95 (4) ◽  
pp. 585-601 ◽  
Author(s):  
Ben P. Kirtman ◽  
Dughong Min ◽  
Johnna M. Infanti ◽  
James L. Kinter ◽  
Daniel A. Paolino ◽  
...  
Keyword(s):  
North American ◽  
Phase 1 ◽  
Phase 2 ◽  
Multimodel Ensemble ◽  
The North ◽  
North American Multimodel Ensemble

scholarly journals Prediction of Seasonal Meteorological Drought Onset and Termination over the Southern Great Plains in the North American Multimodel Ensemble

2020 ◽  
Vol 21 (10) ◽  
pp. 2237-2255
Author(s):  
Richard Seager ◽  
Jennifer Nakamura ◽  
Mingfang Ting
Keyword(s):  
Great Plains ◽  
North American ◽  
Time Scale ◽  
Meteorological Drought ◽  
Atmospheric Variability ◽  
Multimodel Ensemble ◽  
Southern Great Plains ◽  
The North ◽  
Seasonal Time Scale ◽  
North American Multimodel Ensemble

AbstractThe predictability on the seasonal time scale of meteorological drought onsets and terminations over the southern Great Plains is examined within the North American Multimodel Ensemble. The drought onsets and terminations were those identified based on soil moisture transitions in land data assimilation systems and shown to be driven by precipitation anomalies. Sea surface temperature (SST) forcing explains about a quarter of variance of seasonal mean precipitation in the region. However, at lead times of a season, forecast SSTs only explain about 10% of seasonal mean precipitation variance. For the three identified drought onsets, fall 2010 is confidently predicted and spring 2012 is predicted with some skill, and fall 2005 was not predicted at all. None of the drought terminations were predicted on the seasonal time scale. Predictability of drought onset arises from La Niña–like conditions, but there is no indication that El Niño conditions lead to drought terminations in the southern Great Plains. Spring 2012 and fall 2000 are further examined. The limited predictability of onset in spring 2012 arises from cool tropical Pacific SSTs, but internal atmospheric variability played a very important role. Drought termination in fall 2000 was predicted at the 1-month time scale but not at the seasonal time scale, likely because of failure to predict warm SST anomalies directly east of subtropical Asia. The work suggests that improved SST prediction offers some potential for improved prediction of both drought onsets and terminations in the southern Great Plains, but that many onsets and terminations will not be predictable even a season in advance.

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